pierreqi/r_code_50k · Datasets at Hugging Face

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library(dplyr) library(tidyverse) library(readxl) library(lubridate) library(plyr) library(arules) library(arulesViz) #import the data df <- read_excel("C:/Users/khiem.phung/Downloads/Test_Data_Skill.xlsx", sheet = "Data") ###SQL test #first two services and the date df_first2 <- df %>% select(User_id,Serviceid,Date) %>% group_by(User_id) %>% arrange(Date) %>% group_by(User_id) %>% slice(1:2) #last service and the date df_last <- df %>% select(User_id,Serviceid,Date) %>% group_by(User_id) %>% arrange(desc(Date)) %>% group_by(User_id) %>% slice(1) #distinct serviceid that users use df_service <- df %>% distinct(User_id, Serviceid) %>% group_by(User_id) %>% count() #put data in wide format df_first_wide <- df_first2 %>% merge(df_first2[-1,], by = 'User_id') %>% group_by(User_id) %>% slice(1) #merge all tables together df_sql <- df_first_wide %>% inner_join(df_last, by = 'User_id') %>% inner_join(df_service, by = 'User_id') #rename the columns df_sql <- df_sql %>% rename(FirstServiceid = Serviceid.x, FirstServiceDate = Date.x, SecondServiceid = Serviceid.y, SecondServiceDate = Date.y, LastServiceid = Serviceid, LastServiceDate = Date, TotalService = n) #reorder columns df_sql <- df_sql[c(1,2,4,3,5,6,7,8)] ###Analysis test #duplicate to a new df df_sql2 <- df_sql #create customer's age column df_sql2$Customer_age <- (df_sql$LastServiceDate - df_sql$FirstServiceDate) df_sql2$Customer_age <- time_length(df_sql2$Customer_age, unit = 'days') #create table with user id and their age df_age <- df_sql2 %>% select(User_id, Customer_age) #join that table with the original table to get user id, their age, and all #service ids they use df_age <- df_age %>% inner_join(df, by = 'User_id') #select only age groups and all service id used df_age2 <- df_age %>% ungroup(User_id) %>% select(Customer_age, Serviceid) %>% distinct() #initially visualize the clusters using scatterplot ggplot(df_age2, aes(x = Customer_age, y = Serviceid)) + geom_point() #print out serviceid with customer age < 90 df_age2 %>% group_by(Customer_age) %>% filter(Customer_age < 90) %>% ungroup(Customer_age) %>% select(Serviceid) #print out serviceid with customer age > 90 and < 365 df_age2 %>% group_by(Customer_age) %>% filter(Customer_age > 90, Customer_age < 365) %>% ungroup(Customer_age) %>% select(Serviceid) #print out serviceid with customer age > 365 df_age2 %>% group_by(Customer_age) %>% filter(Customer_age > 365) %>% ungroup(Customer_age) %>% select(Serviceid) #get transaction data by putting all service ids on one row, grouped by #users and the date users used those services df_transaction <- ddply(df,c('User_id','Date'), function(df1)paste(df1$Serviceid, collapse = ',')) #select on the service column df_transaction <- df_transaction %>% select(V1) #store the data to a csv file write.csv(df_transaction, 'C:/Users/khiem.phung/Downloads/basket_transaction.csv', quote = FALSE, row.names = FALSE) #load the data into transaction class tr <- read.transactions('C:/Users/khiem.phung/Downloads/basket_transaction.csv', format = 'basket', sep=',') #see the service with most frequent appearance itemFrequencyPlot(tr,topN=10,type="absolute") #mine the rules using the APRIORI algorithm association.rules <- apriori(tr, parameter = list(supp=0.001, conf=0.8)) summary(association.rules) #sort the rules by count association.rules <- sort(association.rules, by="count", decreasing=TRUE) #print top 10 rules inspect(association.rules[1:20]) ##Bonus question #count number of service ids used each date df_date <- df %>% distinct(Date, Serviceid) %>% group_by(Date) %>% count() #visualize the findings ggplot(df_date, aes(x = Date, y = n)) + geom_point() #filter the date to 2018 df_date_2018 <- df_date %>% filter(Date > '2017-12-31') #visualize the new series ggplot(df_date_2018, aes(x = Date, y = n)) + geom_line()

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example_json.R

library(sf) st_read("https://ags.arcdata.cz/arcgis/rest/services/OpenData/AdministrativniCleneni_v12/MapServer/10/query?where=KOD_OBEC%20like%20%27%25554782%25%27&returnGeometry=true&outFields=*&f=json&&resultRecordCount=200") %>% plot(max.plot = 1)

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plot6.R

#libraries library(dplyr) library(ggplot2) # Reading and cleaning data unzip("exdata-data-NEI_data.zip") # This first line will likely take a few seconds. Be patient! NEI <- readRDS("summarySCC_PM25.rds") SCC <- readRDS("Source_Classification_Code.rds") # The following code will filter the SCC file so we can subset the data with only the Motor Vehicle Source # By definition Motor Vehicle sources are considered Onroad Category in USA motorVehicleSCC <- filter(SCC, grepl("[Oo]nroad", SCC$Data.Category)) # The following code will filter only the Motor Vehicle Sources in Baltimore and Los Angeles, then group the data by year # and take the sum for each year motorVehicleNEI <- filter(NEI, SCC %in% motorVehicleSCC$SCC) %>% filter(fips %in% c("24510", "06037")) %>% mutate(fips = factor(fips, levels=c("24510", "06037"), labels=c("Baltimore", "Los Angeles"))) %>% group_by(year, fips) %>% summarise(emissionsSum=sum(Emissions)) # Creating the plot png("plot6.png") ggplot(motorVehicleNEI, aes(year, emissionsSum, color=fips)) + geom_smooth() + xlab("Year") + ylab("Total PM2.5 Emission (in ton)") + ggtitle("Motor Vehicle Sources PM2.5 Emission\n in Baltimore and Los Angeles - 1999 to 2008") dev.off()

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point_to_nearest_coastline.R

# move a point to the nearest coastline point_to_nearest_coastline <- function (bat, loc, mode){ nearest.coastline <- NA dist.to.coast1 <- NA dist.to.coast2 <- NA try(nearest.coastline <- dist2isobath(bat, loc, isobath = -10)) if (!is.na(nearest.coastline[,1][1])){ loc$x <- nearest.coastline[,4] loc$y <- nearest.coastline[,5] dist.to.coast1 <- nearest.coastline[,1][1] / 1000 # meters dist.to.coast2 <- nearest.coastline[,1][2] / 1000 # meters if (mode == 1){ if (loc$x[1] < 0){ loc$x[1] <- loc$x[1] + 360 } if (loc$x[2] < 0){ loc$x[2] <- loc$x[2] + 360 } } } return(data.frame(nearest.coastline, dist.to.coast1, dist.to.coast2, loc$x[1], loc$y[1], loc$x[2], loc$y[2])) }

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SeoulClinic analysis.R

library(ggplot2) data1 <- read.csv("2013년_서울_주요구별_병원현황.csv") data1 barplot(as.matrix(data1[1:9,2:11]), main=paste("서울시 주요구별 과목별 병원현황-2013년", "\n", "출처 "), ylab = "병원수", beside = T, col= rainbow(8)) abline (h=seq(0,350,10), lty=3, lwd=0.2) name <- data1$표시과목 ## to draw in ggplot, you can't use wide table (밑으로 길게 늘어진 형태여야 가능함) ## use melt (data, id=c(새로운 데이터프레임의 칼럼네임이 될 것-기준칼럼)) install.packages("reshape") library(reshape) df_long <- melt(data1, id=c('표시과목')) colnames(df_long) <- c('표시과목','지역명', '의원수') df_long p <- ggplot(df_long, aes(x= 지역명, y=의원수, fill=표시과목)) + geom_bar ######## 쌤 깃허브 보고 할 것

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/generateFrequencies.R \name{generateFreqs} \alias{generateFreqs} \title{Generate cell state frequency distributions for samples} \usage{ generateFreqs( batchStructure, log_prior, clus, fc = 1, cond_induce = "cases", cf_sigma ) } \arguments{ \item{batchStructure}{The structure of the study design in which cases and controls are split into batches. These structures are output by the "distributeSample" functions (which can then be modified if specific structure is desired).} \item{log_prior}{A named vector containing the mean frequencies of the prototype dataset's cell states (log space). The "estimateFreqVar" function returns this a mean frequency vector in linear space (can be transformed into log space via the "log" function).} \item{clus}{The name of the cluster in which a fold change will be induced.} \item{fc}{The magnitude of the fold change that will be induced in the chosen cluster. If no fold change is desired, set fc = 1.} \item{cond_induce}{The condition you wish to induce a fold change in. Setting cond_induce = "cases" will induce a fold change into cases, while setting cond_induce = "ctrls" will induce a fold change into controls.} \item{cf_sigma}{A matrix containing the covariance between cell states. This matrix is received as output from the "estimateFreqVar" function} } \value{ Returns a list containing: a list of cell state frequencies for all case samples and a list of cell state frequencies for all control samples } \description{ Given a batchStructure and baseline frequency distribution (in log space), this function will generate a cell state frequency distribution for each sample. This function also allows users to induce a designated fold change (fc) into either case samples or control samples, and control the magnitude of covariance that cell states have with each other via cf_sigma (e.g. increase or decrease the cell state frequency variation across samples). The magnitude of the fold change will be the ratio of case to control cells (e.g. inducing a fold change of 2 in cases will result in there being, on average, 2 times more case cells than control cells of that cluster }

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Pairwise T Test.r

> library(multcomp, pos=4) > library(abind, pos=4) > AnovaModel.1 <- aov(days.mgraine.5 ~ group, data=Dataset) > summary(AnovaModel.1) Df Sum Sq Mean Sq F value Pr(>F) group 3 76.7 25.5776 2.6472 0.04858 * Residuals 435 4203.0 9.6621 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 37 observations deleted due to missingness > numSummary(Dataset$days.mgraine.5 , groups=Dataset$group, + statistics=c("mean", "sd")) mean sd n NA group A 2.314815 3.911756 108 13 group B 2.027273 1.988310 110 9 group C 2.495495 3.011716 111 7 group D 3.163636 3.229813 110 8 > pairwise.t.test(days.mgraine.5, group, p.adj="bonferroni", paired=T)

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########## # Shiny server functionalities ########## server <- function(input, output, session) { ############## # define static variables for the shiny app: source(system.file("shiny", "shiny_server", "extra_shiny_backend.R", package = "openPrimeRui")) #print("Require namespace test:") #print(requireNamespace("openPrimeRui")) #openPrimeRui:::reset.reactive.values(values = NULL) #stop("TEST") #################### shinyjs::hide(selector = "#light") # don't show traffic light for design difficulty when difficulty hasn't been evaluated yet. shinyjs::hide(id = "loadingContent", anim = TRUE, animType = "fade") # after dependencies have loaded, hide the loading message shinyjs::show("app-content") # show the true app content ############ # convention: reactiveValues (rv) should start with the prefix rv_ ############# # rv_values: other general reactive values that do not fit into existing reactive values # relax_info: bsmodal code when filtering relaxation occurred # last_filtering_constraints: last applied filtering constraints rv_values <- reactiveValues( "relax_info" = NULL, "last_filtering_constraints" = NULL ) ########################### # rv_cur input data: ########################### # templates_exon: template sequence file # templates_leader: allowed binding regions fw file # templates_leader_rev: allowed binding regions rev file # primers: file with primer sequences # settings: xml file for constraint settings rv_cur.input.data <- reactiveValues("templates_exon" = NULL, "templates_leader" = NULL, "templates_leader_rev" = NULL, "primers" = NULL, "settings" = NULL) # load all server source files: sources <- list.files(system.file("shiny", "shiny_server", package = "openPrimeRui"), pattern="server_.*.R", full.names = TRUE) for (s in sources) { #message("Loading shiny server source: ", s) source(s, local = TRUE) } }

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search() install.packages("igraph") library(igraph) search()

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diff_days.R

#' Number of days between two gregorian dates #' #' It determines the number of days between two gregorian dates. It works #' independently from any R date/time function. An advantage of this function #' is that it is accepts dates older than year 1 CE. It uses calendar and #' not astronomical year numbering. #' #' @param year_1 A positive integer #' @param month_1 A positive integer #' @param day_1 A positive integer #' @param bce_1 Logical variable, indicates if the date is Before Common Era #' @param year_2 A positive integer #' @param month_2 A positive integer #' @param day_2 A positive integer #' @param bce_2 Logical variable, indicates if the date is Before Common Era #' #' @examples #' #' diff_days(3114, 8, 11, TRUE, 2012, 12, 21, FALSE) #' #' @export diff_days <- function(from_date, to_date) UseMethod("diff_days") #' @export diff_days.Date <- function(from_date, to_date) { d1 <- as_gregorian_date(from_date) d2 <- as_gregorian_date(to_date) diff_days(d1, d2) } #' @export diff_days.gregorian_date <- function(from_date, to_date) { diff_days2( from_date$year, from_date$month, from_date$day, from_date$bce, to_date$year, to_date$month, to_date$day, to_date$bce ) } diff_days2 <- function(year_1, month_1, day_1, bce_1, year_2, month_2, day_2, bce_2) { month_days <- c(31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31) nd1 <- date_as_number(year_1, month_1, day_1, bce_1) nd2 <- date_as_number(year_2, month_2, day_2, bce_2) if(bce_1) year_1 <- -(year_1 - 1) if(bce_2) year_2 <- -(year_2 - 1) if(nd1 >= nd2) { year_a <- year_2; month_a <- month_2; day_a <- day_2 year_b <- year_1; month_b <- month_1; day_b <- day_1 negative <- TRUE } else { year_a <- year_1; month_a <- month_1; day_a <- day_1 year_b <- year_2; month_b <- month_2; day_b <- day_2 negative <- FALSE } if(year_a == year_b) { if(month_a == month_b) { res <- day_b - day_a } else { md <- month_days if(is_leap_year(year_a)) month_days[[2]] + 1 md <- md[month_a:month_b] md[[1]] <- md[[1]] - day_a md[length(md)] <- day_b res <- sum(md) } } else { yrs <- as.integer(lapply(year_a:year_b, is_leap_year)) yrs <- 365 + yrs md <- month_days md[[1]] <- md[[1]] + is_leap_year(year_a) md <- md[month_a:12] md[[1]] <- md[[1]] - day_a yrs[[1]] <- sum(md) md <- month_days md[[2]] <- md[[2]] + is_leap_year(year_b) md <- md[1:month_b] md[[length(md)]] <- day_b yrs[[length(yrs)]] <- sum(md) res <- sum(yrs) } if(negative) res <- -(res) res }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/examples.R \name{nessy_examples} \alias{nessy_examples} \title{Get a NES example} \usage{ nessy_examples(which = NULL) } \arguments{ \item{which}{The example to run. If empty, all the available examples are listed.} } \value{ A path to the example. } \description{ Get a NES example } \examples{ nessy_examples() }

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library(untb) ### Name: volkov ### Title: Expected frequency of species ### Aliases: volkov ### Keywords: math ### ** Examples ## Not run: ##D volkov(J=21457,c(theta=47.226, m=0.1)) # Example in figure 1 ## End(Not run) volkov(J=20,params=c(theta=1,m=0.4)) data(butterflies) r <- plot(preston(butterflies,n=9,orig=TRUE)) ## Not run: jj <- optimal.params(butterflies) # needs PARI/GP jj <- c(9.99980936124759, 0.991791987473506) points(r,volkov(no.of.ind(butterflies), jj, bins=TRUE),type="b")

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knitAndSave.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/knitAndSave.R \name{knitAndSave} \alias{knitAndSave} \title{knitAndSave} \usage{ knitAndSave( plotToDraw, figCaption, file = NULL, path = NULL, figWidth = ufs::opts$get("ggSaveFigWidth"), figHeight = ufs::opts$get("ggSaveFigHeight"), units = ufs::opts$get("ggSaveUnits"), dpi = ufs::opts$get("ggSaveDPI"), catPlot = ufs::opts$get("knitAndSave.catPlot"), ... ) } \arguments{ \item{plotToDraw}{The plot to knit using \code{\link[=knitFig]{knitFig()}} and save using \code{\link[=ggSave]{ggSave()}}.} \item{figCaption}{The caption of the plot (used as filename if no filename is specified).} \item{file, path}{The filename to use when saving the plot, or the path where to save the file if no filename is provided (if \code{path} is also omitted, \code{getWd()} is used).} \item{figWidth, figHeight}{The plot dimensions, by default specified in inches (but 'units' can be set which is then passed on to \code{\link[=ggSave]{ggSave()}}.} \item{units, dpi}{The units and DPI of the image which are then passed on to \code{\link[=ggSave]{ggSave()}}.} \item{catPlot}{Whether to use \code{\link[=cat]{cat()}} to print the knitr fragment.} \item{...}{Additional arguments are passed on to \code{\link[=ggSave]{ggSave()}}. Note that file (and ...) are vectorized (see the \code{\link[=ggSave]{ggSave()}} manual page).} } \value{ The \code{\link[=knitFig]{knitFig()}} result, visibly. } \description{ knitAndSave } \examples{ \dontrun{plot <- ggBoxplot(mtcars, 'mpg'); knitAndSave(plot, figCaption="a boxplot", file=tempfile(fileext=".png"));} }

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/Getting and Cleaning Data/Week 2/quiz.R

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quiz.R

library(httr) library(sqldf) # PROBLEM 1 oauth_endpoints("github") myapp <- oauth_app("github", key = "e60d1c464f658054d81a", secret = "5fde2ac1529b22d3cf1d4b0b0a38005478a9ff3d" ) # Get OAuth credentials github_token <- oauth2.0_token(oauth_endpoints("github"), myapp) gtoken <- config(token = github_token) # Use API req <- with_config(gtoken, GET("https://api.github.com/users/jtleek/repos")) con_request <- content(req) find_create <- function(x,myurl) { if (x$html_url == myurl) { print(x$created_at) } } lapply(con_request, find_create, myurl ="https://github.com/jtleek/datasharing") # Problem 2 acs <- read.csv("getdata_data_ss06pid.csv") head(sqldf("select pwgtp1 from acs where AGEP < 50")) # Problem 3 sqldf("select distinct AGEP from acs") # Problem 4 con = url("http://biostat.jhsph.edu/~jleek/contact.html") htmlCode = readLines(con) close(con) nchar(htmlCode[100]) # Problem 5 data <- read.fwf("getdata_wksst8110.for", skip=4, widths=c(12, 7, 4, 9, 4, 9, 4, 9, 4)) sum(data[,4])

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/man/gen.arch.wge.Rd

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cran/tswge

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2023-04-01T22:52:04.649970

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gen.arch.wge.Rd

\name{gen.arch.wge} \alias{gen.arch.wge} \title{Generate a realization from an ARCH(q0) model} \description{Generates a realization of length n from the GARCH(q0) model (4.23) in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott} \usage{ gen.arch.wge(n, alpha0, alpha, plot = TRUE,sn=0) } \arguments{ \item{n}{Length of realization to be generated} \item{alpha0}{The constant alpha0 in model (4.23)} \item{alpha}{A vector of length q0 containing alpha1 through alphaq0} \item{plot}{If plot=TRUE (default) the generated realization is plotted} \item{sn}{determines the seed used in the simulation. sn=0 produces new/random realization each time. sn=positive integer produces same realization each time} } \value{returns the generated realization} \references{"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott} \author{Wayne Woodward} \examples{gen.arch.wge(n=200,alpha0=.1,alpha=c(.36,.27,.18,.09))} \keyword{ ARCH } \keyword{ Conditional variance}

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/R_coco/crawling_basic2.R

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edgestory/DS

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crawling_basic2.R

library(stringr) getwd() data <- read.csv("final_data.csv") head(data) url_list <- data[,3] length(url_list) content <- c() for ( i in 1:length(url_list)){ ## try_error if(class(try(b<-readLines(as.character(url_list[i]), encoding = 'UTF-8'))) == "try-error"){ b6 <- "" content <- c(content,b6) # next; }else{ b2<-b[which(str_detect(b,"post_content")):which(str_detect(b,"post_ccls"))] b3<-paste(b2, collapse = "") b4 <- gsub("<.*?>","",b3) b5 <- gsub("\t|&nbsp","",b4) b6 <- str_trim(b5) content <- c(content) cat("\n",i) } }

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/inlabru/R/bru.integration.R

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albrizre/spatstat.revdep

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bru.integration.R

#' @title Generate integration points #' #' @description #' This function generates points in one or two dimensions with a weight attached to each point. #' The weighted sum of a function evaluated at these points is the integral of that function approximated #' by linear basis functions. The parameter \code{region} describes the area(s) integrated over. #' #' In case of a single dimension \code{region} is supposed to be a two-column \code{matrix} where #' each row describes the start and end point of the interval to integrate over. In the two-dimensional #' case \code{region} can be either a \code{SpatialPolygon}, an \code{inla.mesh} or a #' \code{SpatialLinesDataFrame} describing the area to integrate over. If a \code{SpatialLineDataFrame} #' is provided it has to have a column called 'weight' in order to indicate the width of the line. #' #' The domain parameter is an \code{inla.mesh.1d} or \code{inla.mesh} object that can be employed to #' project the integration points to the vertices of the mesh. This reduces the final number of #' integration points and reduces the computational cost of the integration. The projection can also #' prevent numerical issues in spatial LGCP models where each observed point is ideally surrounded #' by three integration point sitting at the coresponding mesh vertices. For convenience, the #' \code{domain} parameter can also be a single integer setting the number of equally spaced integration #' points in the one-dimensional case. #' #' @aliases ipoints #' @export #' #' @author Fabian E. Bachl <\email{bachlfab@@gmail.com}> #' #' @param region Description of the integration region boundary. #' In 1D either a vector of two numerics or a two-column matrix where each row describes and interval. #' In 2D either a \code{SpatialPolygon} or a \code{SpatialLinesDataFrame} with a weight column defining the width of the line. #' @param domain In 1D a single numeric setting the numer of integration points or an \code{inla.mesh.1d} #' defining the locations to project the integration points to. In 2D \code{domain} has to be an #' \code{inla.mesh} object describing the projection and granularity of the integration. #' @param name Character array stating the name of the domains dimension(s) #' @param group Column names of the \code{region} object (if applicable) for which the integration points are calculated independently and not merged by the projection. #' @param project If TRUE, project the integration points to mesh vertices #' #' @return A \code{data.frame} or \code{SpatialPointsDataFrame} of 1D and 2D integration points, respectively. #' #' @examples #' \donttest{ #' if (require("INLA", quietly = TRUE)) { #' #' # Create 50 integration points covering the dimension 'myDim' between 0 and 10. #' #' ips = ipoints(c(0,10), 50, name = "myDim") #' plot(ips) #' #' # Create integration points for the two intervals [0,3] and [5,10] #' #' ips = ipoints(matrix(c(0,3, 5,10), nrow = 2, byrow = TRUE), 50) #' plot(ips) #' #' # Convert a 1D mesh into integration points #' mesh = inla.mesh.1d(seq(0,10,by = 1)) #' ips = ipoints(mesh, name = "time") #' plot(ips) #' #' #' # Obtain 2D integration points from a SpatialPolygon #' #' data(gorillas, package = "inlabru") #' ips = ipoints(gorillas$boundary) #' ggplot() + gg(gorillas$boundary) + gg(ips, aes(size = weight)) #' #' #' #' Project integration points to mesh vertices #' #' ips = ipoints(gorillas$boundary, domain = gorillas$mesh) #' ggplot() + gg(gorillas$mesh) + gg(gorillas$boundary) + gg(ips, aes(size = weight)) #' #' #' # Turn a 2D mesh into integration points #' #' ips = ipoints(gorillas$mesh) #' ggplot() + gg(gorillas$boundary) + gg(ips, aes(size = weight)) #' } #' } ipoints = function(region = NULL, domain = NULL, name = "x", group = NULL, project) { pregroup = NULL # If region is null treat domain as the region definition if ( is.null(region) ) { if ( is.null(domain) ) { stop("regio and domain can not be NULL at the same time.") } else { region = domain ; domain = NULL } } if ( is.data.frame(region) ) { if (!("weight" %in% names(region))) { region$weight = 1 } ips = region } else if (is.integer(region)){ ips = data.frame(weight = rep(1,length(region))) ips[name] = region } else if (is.numeric(region)) { if ( is.null(dim(region)) ){ region = matrix(region, nrow = 1) } if ( ncol(region) == 1) { ips = data.frame(x = region[,1], weight = 1) colnames(ips) = c(name, "weight") } else { ips = list() for (j in 1:nrow(region) ) { subregion = region[j,] # If domain is NULL set domain to a 1D mesh with 30 equally spaced vertices and boundary according to region # If domain is a single numeric set domain to a 1D mesh with n=domain vertices and boundary according to region if ( is.null(domain) ) { subdomain = INLA::inla.mesh.1d(seq(min(subregion), max(subregion), length.out = 30)) } else if ( is.numeric(domain)) { subdomain = INLA::inla.mesh.1d(seq(min(subregion), max(subregion), length.out = domain)) } else { subdomain = stop("1D weight projection not yet implemented") } fem = INLA::inla.mesh.1d.fem(subdomain) ips[[j]] = data.frame(weight = Matrix::diag(fem$c0)) ips[[j]][name] = subdomain$loc ips[[j]] = ips[[j]][,c(2,1)] # make weights second column } ips = do.call(rbind, ips) } } else if ( inherits(region, "inla.mesh") ){ # If domain is provided: break if ( !is.null(domain) ) stop("Integration region provided as 2D and domain is not NULL.") # transform to equal area projection if ( !is.null(region$crs) && !(is.na(region$crs@projargs))) { crs = region$crs region = stransform(region, crs = CRS("+proj=cea +units=km")) } ips = vertices(region) ips$weight = INLA::inla.mesh.fem(region, order = 1)$va # backtransform if ( !is.null(region$crs) && !(is.na(region$crs@projargs))) { ips = stransform(ips, crs = crs) } } else if ( inherits(region, "inla.mesh.1d") ){ ips = data.frame(x = region$loc) colnames(ips) = name ips$weight = Matrix::diag(INLA::inla.mesh.fem(region)$c0) } else if ( class(region) == "SpatialPoints" ){ ips = region ips$weight = 1 } else if ( class(region) == "SpatialPointsDataFrame" ){ if (!("weight" %in% names(region))) { warning("The integration points provided have no weight column. Setting weights to 1.") region$weight = 1 } ips = region } else if ( inherits(region, "SpatialLines") || inherits(region, "SpatialLinesDataFrame") ){ # If SpatialLines are provided convert into SpatialLinesDataFrame and attach weight = 1 if ( class(region)[1] == "SpatialLines" ) { region = SpatialLinesDataFrame(region, data = data.frame(weight = rep(1, length(region)))) } # Set weights to 1 if not provided if (!("weight" %in% names(region))) { warning("The integration points provided have no weight column. Setting weights to 1.") region$weight = 1 } ips = int.slines(region, domain, group = group) } else if (inherits(region,"SpatialPolygons")){ # If SpatialPolygons are provided convert into SpatialPolygonsDataFrame and attach weight = 1 if ( class(region)[1] == "SpatialPolygons" ) { region = SpatialPolygonsDataFrame(region, data = data.frame(weight = rep(1, length(region))), match.ID = FALSE) } cnames = coordnames(region) p4s = proj4string(region) # Convert region and domain to equal area CRS if ( !is.null(domain$crs) && !is.na(domain$crs@projargs)){ region = stransform(region, crs = CRS("+proj=cea +units=km")) } polyloc = do.call(rbind, lapply(1:length(region), function(k) cbind( x = rev(coordinates(region@polygons[[k]]@Polygons[[1]])[,1]), y = rev(coordinates(region@polygons[[k]]@Polygons[[1]])[,2]), group = k))) # If domain is NULL, make a mesh with the polygons as boundary if ( is.null(domain) ) { max.edge = max(diff(range(polyloc[,1])), diff(range(polyloc[,2])))/20 domain = INLA::inla.mesh.2d(boundary = region, max.edge = max.edge) domain$crs = CRS(proj4string(region)) } else { if ( !is.null(domain$crs) && !is.na(domain$crs@projargs)) domain = stransform(domain, crs = CRS("+proj=cea +units=km")) } ips = int.polygon(domain, loc = polyloc[,1:2], group = polyloc[,3]) df = data.frame(region@data[ips$group, pregroup, drop = FALSE], weight = ips[,"weight"]) ips = SpatialPointsDataFrame(ips[,c("x","y")], data = df, match.ID = FALSE) proj4string(ips) = proj4string(region) if ( !is.na(p4s) ) { ips = stransform(ips, crs = CRS(p4s)) } } ips } #' @title Cross product of integration points #' #' @description #' Calculates the cross product of integration points in different dimensions #' and multiplies their weights accordingly. If the object defining points in a particular #' dimension has no weights attached to it all weights are assumend to be 1. #' #' @aliases cprod #' @export #' #' @author Fabian E. Bachl <\email{bachlfab@@gmail.com}> #' #' @param ... \code{data.frame} or \code{SpatialPointsDataFrame} objects, each one usually obtained by a call to the \link{ipoints} function. #' @return A \code{data.frame} or \code{SpatialPointsDataFrame} of multidimensional integration points and their weights #' #' @examples #' \donttest{ #' # ipoints needs INLA #' if (require("INLA", quietly = TRUE)) { #' # Create integration points in dimension 'myDim' and 'myDiscreteDim' #' ips1 = ipoints(c(0,8), name = "myDim") #' ips2 = ipoints(as.integer(c(1,2,3)), name = "myDiscreteDim") #' #' # Calculate the cross product #' ips = cprod(ips1, ips2) #' #' # Plot the integration points #' plot(ips$myDim, ips$myDiscreteDim, cex = 10*ips$weight) #' } #' } cprod = function(...) { ipl = list(...) ipl = ipl[!vapply(ipl, is.null, TRUE)] if ( length(ipl) == 0 ) return(NULL) if ( length(ipl) == 1 ) { ips = ipl[[1]] } else { ips1 = ipl[[1]] ips2 = do.call(cprod, ipl[2:length(ipl)]) if (! "weight" %in% names(ips1) ) { ips1$weight = 1 } if (! "weight" %in% names(ips2) ) { ips2$weight = 1 } loc1 = ips1[,setdiff(names(ipl[[1]]),"weight"), drop = FALSE] w1 = data.frame(weight = ips1$weight) loc2 = ips2[,setdiff(names(ips2),"weight"), drop = FALSE] w2 = data.frame(weight2 = ips2[,"weight"]) # Merge the locations. In case of Spatial objects we need to use the sp:merge # function. Unfortunately sp::merge replicates entries in a different order than # base merge so we need to reverse the order of merging the weights if ( inherits(loc1, "Spatial") ) { ips = sp::merge(loc1, loc2, duplicateGeoms = TRUE) weight = merge(w2, w1) } else if ( inherits(loc2, "Spatial") ){ ips = sp::merge(loc2, loc1, duplicateGeoms = TRUE) weight = merge(w2, w1) } else { ips = merge(loc1, loc2) weight = merge(w1, w2) } ips$weight = weight$weight * weight$weight2 } ips } # Integration points for log Gaussian Cox process models using INLA # # prerequisits: # # - List of integration dimension names, extend and quadrature # - Samplers: These may live in a subset of the dimensions, usually space and time # ("Where and wehen did a have a look at the point process") # - Actually this is a simplified view. Samplers should have start and end time ! # # Procedure: # - Select integration strategy by type of samplers: # 1) SpatialPointsDataFrame: Assume these are already integration points # 2) SpatialLinesDataFrame: Use simplified integration along line with (width provided by samplers) # 3) SpatialPolygonDataFrame: Use full integration over polygons # # - Create integration points from samplers. Do NOT perform simplification projection here! # - Simplify integration points. # 1) Group by non-mesh dimensions, e.g. time, weather # 2) For each group simplify with respect to mesh-dimensions, e.g. space # 3) Merge # # Dependencies (iDistance): # int.points(), int.polygon(), int.1d(), int.expand(), recurse.rbind() # # @aliases ipoints # @export # @param samplers A Spatial[Points/Lines/Polygons]DataFrame objects # @param points A SpatialPoints[DataFrame] object # @param config An integration configuration. See \link{iconfig} # @return Integration points ipmaker = function(samplers, domain, dnames, model = NULL, data = NULL) { # Fill missing domain definitions using meshes from effects where map equals the domain name meshes = list() for (e in effect(model)) {meshes[[paste0(as.character(e$map), collapse ="")]] = e$mesh} for ( nm in dnames) { if ( is.null(domain[[nm]]) ) { domain[[nm]] = meshes[[nm]] } } # Fill missing domain definitions with data ranges for ( nm in dnames) { if ( !(nm %in% names(domain)) & !is.null(data) & !(nm %in% names(samplers))){ if ( nm == "coordinates" ) { domain[["coordinates"]] = INLA::inla.mesh.2d(loc.domain = coordinates(data), max.edge = diff(range(coordinates(data)[,1]))/10) domain[["coordinates"]]$crs = INLA::inla.CRS(proj4string(data)) } else { domain[[nm]] = range(data[[nm]]) } } } if ( "coordinates" %in% dnames ) { spatial = TRUE } else { spatial = FALSE } # Dimensions provided via samplers (except "coordinates") samp.dim = intersect(names(samplers), dnames) # Dimensions provided via domain but not via samplers nosamp.dim = setdiff(names(domain), c(samp.dim, "coordinates")) # Check if a domain definition is missing missing.dims = setdiff(dnames, c(names(domain), samp.dim)) if ( length(missing.dims > 0) ) stop(paste0("Domain definitions missing for dimensions: ", paste0(missing.dims, collapse = ", "))) if ( spatial ) { ips = ipoints(samplers, domain$coordinates, project = TRUE, group = samp.dim) } else { ips = NULL } lips = lapply(nosamp.dim, function(nm) ipoints(NULL, domain[[nm]], name = nm)) ips = do.call(cprod, c(list(ips), lips)) } # Project data to mesh vertices under the assumption of lineariity # # # @aliases vertex.projection # @export # @param points A SpatialPointsDataFrame object # @param mesh An inla.mesh object # @param columns A character array of the points columns which whall be projected # @param group Character array identifying columns in \code{points}. These coloumns are interpreted as factors and the projection is performed independently for eah combination of factor levels. # @return SpatialPointsDataFrame of mesh vertices with projected data attached vertex.projection = function(points, mesh, columns = names(points), group = NULL, fill = NULL){ if ( is.null(group) | (length(group) == 0) ) { res = INLA::inla.fmesher.smorg(mesh$loc, mesh$graph$tv, points2mesh = coordinates(points)) tri = res$p2m.t data = list() for (k in 1:length(columns)){ cn = columns[k] nw = points@data[,columns] * res$p2m.b w.by = by(as.vector(nw), as.vector(mesh$graph$tv[tri,]), sum, simplify = TRUE) data[[cn]] = as.vector(w.by) } data = data.frame(data) coords = mesh$loc[as.numeric(names(w.by)),c(1,2)] data$vertex = as.numeric(names(w.by)) ret = SpatialPointsDataFrame(coords, proj4string = CRS(proj4string(points)), data = data, match.ID = FALSE) coordnames(ret) = coordnames(points) # If null is not not NULL, add vertices to which no data was projected # and set their projected data according to `fill` if ( !is.null(fill) ) { vrt = vertices(mesh) vrt = vrt[setdiff(vrt$vertex, data$vertex),] if ( nrow(vrt) > 0 ){ for (nm in setdiff(names(data), "vertex")) vrt[[nm]] = fill ret = rbind(ret, vrt) } ret = ret[match(1:mesh$n, ret$vertex),] } } else { fn = function(X) { coordinates(X) = coordnames(points) ret = vertex.projection(X, mesh, columns = columns) for (g in group) { ret[[g]] = X[[g]][1] } ret } idx = as.list(data.frame(points)[,group,drop=FALSE]) ret = by(points, idx, fn) ret = do.call(rbind, ret) proj4string(ret) = proj4string(points) } ret } # Project data to mesh vertices under the assumption of lineariity # # # @aliases vertex.projection # @export # @param points A SpatialPointsDataFrame object # @param mesh An inla.mesh object # @param columns A character array of the points columns which whall be projected # @param group Character array identifying columns in \code{points}. These coloumns are interpreted as factors and the projection is performed independently for eah combination of factor levels. # @return SpatialPointsDataFrame of mesh vertices with projected data attached # @example # # pts = data.frame(x = 50 * runif(10), weight = abs(rnorm(100))) # msh = inla.mesh.1d(seq(0,50,by=1)) # pts$year = c(rep(1,5), rep(2,5)) # ip = vertex.projection.1d(pts, msh) # ggplot(ip) + geom_point(aes(x=x, y=weight)) # # ip = vertex.projection.1d(pts, msh, group = "year", fill = 0, column = "weight") # head(ip) # ggplot(ip) + geom_point(aes(x=x, y=weight, color = year)) vertex.projection.1d = function(points, mesh, group = NULL, column = "weight", simplify = TRUE, fill = NULL) { dname = setdiff(names(points),c(column, group)) if ( length(dname)>1 ) { dname = dname[1] } xx = points[, dname] ww = points[, column] iv = findInterval(xx, mesh$loc) # Left and right vertex location left = mesh$loc[iv] right = mesh$loc[iv+1] # Relative location within the two neighboring vertices w.right = (xx-left)/(right-left) w.left = 1 - w.right # Projected integration points ips = rbind(data.frame(x = left, vertex = iv), data.frame(x = right, vertex = iv+1)) ips[column] = c(ww * w.left, ww * w.right) # Simplify if ( simplify ) { bygroup = list(vertex = ips$vertex) if ( !is.null(group) ) { bygroup = c(bygroup, as.list(rbind(points[,group,drop=FALSE], points[,group,drop=FALSE]))) } ips = aggregate(ips[,column, drop = FALSE], by = bygroup, FUN = sum) } # Add x-coordinate ips[dname] = mesh$loc[ips$vertex] # Fill if ( !is.null(fill) ) { miss = setdiff(1:length(mesh$loc), ips$vertex) mips = data.frame(vertex = miss, x = mesh$loc[miss]) mips[,column] = fill ips = rbind(ips, merge(mips, ips[,group, drop=FALSE])) } ips } #' Weighted summation (integration) of data frame subsets #' #' A typical task in statistical inference to integrate a (multivariate) function along one or #' more dimensions of its domain. For this purpose, the function is evaluated at some points #' in the domain and the values are summed up using weights that depend on the area being #' integrated over. This function performs the weighting and summation conditional for each level #' of the dimensions that are not integrated over. The parameter \code{dims} states the the #' dimensions to integrate over. The set of dimensions that are held fixed is the set difference #' of all column names in \code{data} and the dimensions stated by \code{dims}. #' #' @aliases int #' @export #' @param data A \code{data.frame} or \code{Spatial} object. Has to have a \code{weight} column with numeric values. #' @param values Numerical values to be summed up, usually the result of function evaluations. #' @param dims Column names (dimension names) of the \code{data} object to integrate over. #' @return A \code{data.frame} of integrals, one for each level of the cross product of all dimensions not being integrated over. #' #' @examples #' \donttest{ #' # ipoints needs INLA #' if (require("INLA", quietly = TRUE)) { #' # Create integration points in two dimensions, x and y #' #' ips = cprod(ipoints(c(0,10), 10, name = "x"), #' ipoints(c(1,5), 10, name = "y")) #' #' # The sizes of the domains are 10 and 4 for x and y, respectively. #' # Integrating f(x,y) = 1 along x and y should result in the total #' # domain size 40 #' #' int(ips, rep(1, nrow(ips)), c("x","y")) #' } #' } int = function(data, values, dims = NULL) { keep = setdiff(names(data), c(dims, "weight")) if (length(keep) > 0 & !is.null(dims)) { agg = aggregate(values * data$weight, by = as.list(data[,keep,drop=FALSE]), FUN = sum) names(agg)[ncol(agg)] = "integral" # paste0("integral_{",dims,"}(",deparse(values),")") } else { agg = sum(data$weight * values) } agg }

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# Functions to modify and organize the dataframes from the VP output and get ready for plotting doItAll_dataWrangling <- function(outPath, scenarioNum, indSites = FALSE){ if(indSites == TRUE){ richness <- readRDS(paste0(outPath, scenarioNum, "-metacomSim.RDS")) %>% set_names(imap(., ~ paste0("iter", .y))) %>% map(., rowSums) %>% bind_rows() %>% rownames_to_column(var = "sites") %>% gather(., key = "iteration", value = "richness", -sites) %>% mutate(identifier = paste0("site", sites, "_", iteration)) %>% select(., -c(sites, iteration)) vp <- readRDS(paste0(outPath, scenarioNum, "-vpsites.RDS")) overlap1 <- map(vp, "overlap1") overlap2 <- map(vp, "overlap2") overlap3 <- map(vp, "overlap3") vpALL <- vector("list", length = 5) for(i in 1:5){ workingVP1 <- overlap1[[i]] workingVP2 <- overlap2[[i]] workingVP3 <- overlap3[[i]] c <- rowSums(workingVP1[,,1])/15 b <- rowSums(workingVP1[,,2])/15 a <- rowSums(workingVP1[,,3])/15 e <- rowSums(workingVP2[,,1])/15 f <- rowSums(workingVP2[,,2])/15 d <- rowSums(workingVP2[,,3])/15 g <- rowSums(workingVP3)/15 env <- a + f + 1/2 * d + 1/2 * g env <- ifelse(env < 0, 0, env) spa <- b + e + 1/2 * d + 1/2 * g spa <- ifelse(spa < 0, 0, spa) random <- c codist <- ifelse(random < 0, 0, random) r2 <- env + spa + codist iteration <- factor(paste0("iter", i), levels = paste0("iter", 1:5)) cleanData <- cbind.data.frame(env, spa, codist, r2, iteration) cleanData$site <- paste0(row.names(cleanData)) vpALL[[i]] <- cleanData } vpALL %>% bind_rows() %>% mutate(identifier = paste(site, iteration, sep = "_"), scenario = scenarioNum) %>% left_join(., richness) -> vpALL return(vpALL) } params <- with(readRDS(paste0(outPath, scenarioNum, "-params.RDS")), { enframe(u_c[1,], name = "species",value = "nicheOpt") %>% left_join(., enframe(s_c[1,], name = "species", value = "nicheBreadth")) %>% left_join(., enframe(c_0, name = "species", value = "colProb")) %>% mutate(dispersal = alpha, species = as.character(species), intercol = d_c, interext = d_e) }) prevalence <- readRDS(paste0(outPath, scenarioNum, "-metacomSim.RDS")) %>% set_names(imap(., ~ paste0("iter_", .y))) %>% map(., colSums) %>% bind_cols() %>% rownames_to_column(var = "species") %>% gather(., key = "iteration", value = "prevalence", -species) %>% mutate(identifier = paste0("spp", species, "_", iteration)) %>% select(., -c(species, iteration)) readRDS(paste0(outPath, scenarioNum, "-vpspp.RDS")) %>% set_names(imap(., ~ paste0("iter_", .y))) -> VPdata fullData <- list() for(i in 1:length(VPdata)){ fullData[[i]] <- VPdata[[i]] %>% map(as_tibble) %>% bind_cols() %>% rownames_to_column() %>% set_names(c("species", "c", "b", "a", "e", "f", "d", "g")) %>% transmute(species = species, env = a + f + 0.5 * d + 0.5 * g, env = ifelse(env < 0, 0, env), spa = b + e + 0.5 * d + 0.5 * g, spa = ifelse(spa < 0, 0, spa), codist = c, codist = ifelse(codist < 0, 0, codist), r2 = env + spa + codist, iteration = names(VPdata[i])) %>% left_join(., params) } fullData %>% bind_rows() %>% mutate(identifier = paste0("spp", species, "_", iteration), scenario = scenarioNum) %>% left_join(., prevalence) -> fullData return(fullData) } # csv and figures --------------------------------------------------------- save_csv_and_plots <- function(scenario){ sppcsv <- doItAll_dataWrangling(outPath = folderpath, scenarioNum = scenario, indSites = FALSE) write.csv(sppcsv, file = paste0(folderpath, "csvFiles/", scenario, "spp.csv")) sppcsv %>% make_tern_plot(., varShape = "iteration", varColor = "nicheOpt") + labs(title = scenario) ggsave(filename = paste0(folderpath, "figures/", scenario, "spp.png"), dpi = 300, width = 9, height = 4.5) sitescsv <- doItAll_dataWrangling(outPath = folderpath, scenarioNum = scenario, indSites = TRUE) write.csv(sitescsv, file = paste0(folderpath, "csvFiles/", scenario, "sites.csv")) make_tern_plot(sitescsv, varShape = "iteration", varColor = "richness") + labs(title=scenario) ggsave(filename = paste0(folderpath, "figures/", scenario, "sites.png"), dpi = 300, width = 9, height = 4.5) }

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# checking your data is crucial to do good analysis! Here some methods are shown # install the mice packages to check your dataset for NA's and use md.pattern to # find missing datapoints. IF you get all 1 and 0 below you are golden install.packages('mice') library(mice) md.pattern(data) # use str() to figure out if some parts of your data should be a factor or not # then use as.factor() to change the values to a factor data = morley str(data) data$Expt = as.factor(data$Expt) str(data) # depending on the data typ you can now choose to inspect the data further with # for example a boxplot to do that, use ggplot or build in boxplot() function. # ggplot example install.packages('ggplot2') library(ggplot2) ggplot(data = data, aes(Expt,Speed)) + geom_boxplot() + stat_boxplot(geom = 'errorbar', width = 0.3) # basic r example boxplot(data$Speed~data$Expt) # one can use a different approach install.packages('sciplot') library(sciplot) bargraph.CI(Expt, Speed, col = (gray(0.88)), data = data, xlab = "Experiment", ylab = "count", ylim = c(0,20)) # lineplot.CI(Expt, Speed, type = "p", data = data, xlab = "spray", ylab = "count", ylim = c(0,20))

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Maronna.Rd.R

library(clues) ### Name: Maronna ### Title: The Maronna Data Set ### Aliases: Maronna maronna maronna.mem ### Keywords: cluster ### ** Examples data(Maronna) # data matrix maronna <- Maronna$maronna # cluster membership maronna.mem <- Maronna$maronna.mem # 'true' number of clusters nClust <- length(unique(maronna.mem)) # scatter plots plotClusters(maronna, maronna.mem)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/coef.GGM_bootstrap.R \name{coef.estimate} \alias{coef.estimate} \alias{coef.GGM_bootstrap} \title{Precision Matrix to Multiple Regression} \usage{ \method{coef}{GGM_bootstrap}(object, node = 1, ci = 0.95, ...) } \arguments{ \item{object}{object of class \code{estimate} (analytic = F)} \item{node}{which variable (node) to summarise} \item{ci}{confidence interval used in the summary output} \item{...}{currently ignored} } \value{ list of class \code{coef.estimate}: } \description{ There is a direct correspondence between the covariance matrix and multiple regression. In the case of GGMs, it is possible to estimate the edge set with multiple regression (i.e., neighborhood selection). In \strong{GGMnonreg}, the precision matrix is first bootstrapped, and then each sample is converted to the corresponding coefficients and error variances. This results in bootstrap distributions for a multiple regression. } \examples{ # data X <- scale(GGMnonreg::ptsd) # fit model fit <- GGM_bootstrap(X) # summary for predicting the first variable coef(fit, node = 1) }

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# install.packages("ggdendro") # install.packages("reshape2") # install.packages("grid") # install.packages("dendextend") library(dplyr) projects <- data.frame( #id.s = c(1:58), pro_involved = c("+C", "1D", "+D", "+C", "+C", "+D", "+D", "+D", "1D", "1D", "+C", "+D", "+C", "+D", "+C", "+C", "+C", "+D", "+C", "+C", "+C", "1D", "+D", "+C", "1D", "+C", "+D", "+C", "+C", "+C", "+C", "+D", "+C", "+D", "+D", "+D", "+D", "+C", "+D", "+C", "+C", "+C", "+C", "+C", "+C", "+D", "+C", "+D", "+C", "+C", "+C", "+C", "+C", "+C", "+C", "+C", "+C", "+C"), pro_focus = c("P&S", "OE&A", "I&DM", "I&DM", "C", "OE&A", "OE&A", "OE&A", "OE&A", "OE&A", "P&S", "P&S", "C", "OE&A", "P&S", "OE&A", "C", "I&DM", "C", "I&DM", "OE&A", "I&DM", "C", "P&S", "I&DM", "C", "OE&A", "OE&A", "I&DM", "I&DM", "OE&A", "I&DM", "I&DM", "OE&A", "OE&A", "OE&A", "OE&A", "C", "I&DM", "I&DM", "I&DM", "P&S", "I&DM", "P&S", "I&DM", "OE&A", "C", "P&S", "P&S", "OE&A", "OE&A", "C", "OE&A", "I&DM", "OE&A", "OE&A", "I&DM", "P&S"), pro_complexity = c("H/H", "L/L", "L/H", "H/H", "H/H", "H/L", "H/L", "H/H", "H/H", "H/H", "L/H", "H/L", "L/H", "H/H", "L/H", "H/H", "L/H", "H/H", "H/H", "H/H", "L/H", "H/H", "H/L", "H/H", "H/L", "H/H", "H/H", "H/L", "H/L", "H/H", "L/L", "L/H", "H/L", "L/L", "H/H", "L/L", "H/H", "H/H", "L/H", "L/H", "H/H", "H/H", "H/H", "L/H", "H/H", "H/H", "H/L", "H/L", "H/H", "H/L", "L/L", "L/L", "H/L", "L/L", "H/L", "H/L", "L/H", "H/H"), pro_impact = c("+R", "-C", "+R", "-C", "+R", "-C", "+R", "-C", "O", "O", "+R", "O", "+R", "O", "O", "-C", "+R", "O", "+R", "O", "-C", "O", "+R", "+R", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "-C", "-C", "-C", "+R", "O", "-C", "O", "+R", "O", "O", "O", "-C", "+R", "+R", "+R", "+R", "O", "-C", "-C", "-C", "-C", "-C", "O", "+R"), pro_mode = c("explore", "explore", "explore", "exploit", "exploit", "exploit", "explore", "exploit", "exploit", "exploit", "explore", "exploit", "exploit", "exploit", "exploit", "exploit", "exploit", "exploit", "explore", "explore", "exploit", "exploit", "explore", "explore", "exploit", "explore", "exploit", "exploit", "exploit", "explore", "explore", "exploit", "exploit", "exploit", "exploit", "exploit", "exploit", "explore", "exploit", "exploit", "explore", "exploit", "exploit", "exploit", "explore", "exploit", "explore", "explore", "exploit", "exploit", "exploit", "exploit", "exploit", "exploit", "exploit", "exploit", "exploit", "explore"), pro_pathway = c("CX", "CX", "I&S", "I&S", "CX", "iterate", "new", "I&S", "I&S", "I&S", "CX", "CX", "CX", "I&S", "new", "new", "new", "I&S", "new", "I&S", "I&S", "I&S", "CX", "I&S", "I&S", "new", "I&S", "I&S", "new", "new", "I&S", "I&S", "I&S", "I&S", "I&S", "I&S", "I&S", "CX", "CX", "I&S", "CX", "CX", "new", "CX", "CX", "I&S", "CX", "CX", "CX", "I&S", "CX", "I&S", "new", "I&S", "I&S", "I&S", "I&S", "iterate"), stringsAsFactors=TRUE ) #----- Dissimilarity Matrix -----# library(cluster) # to perform different types of hierarchical clustering # package functions used: daisy(), diana(), clusplot() gower.dist <- daisy(projects[ ,1:6], metric = c("gower")) # class(gower.dist) ## dissimilarity , dist #------------ DIVISIVE CLUSTERING ------------# divisive.clust <- diana(as.matrix(gower.dist), diss = TRUE, keep.diss = TRUE) plot(divisive.clust, main = "Divisive") #------------ AGGLOMERATIVE CLUSTERING ------------# aggl.clust.c <- hclust(gower.dist, method = "complete") plot(aggl.clust.c, main = "Agglomerative, complete linkages") # Cluster stats comes in a list form, it is more convenient to look at it as a table # This code below will produce a dataframe with observations in columns and variables in row # Not quite tidy data, but it's nicer to look at library(fpc) cstats.table <- function(dist, tree, k) { clust.assess <- c("cluster.number","n","within.cluster.ss","average.within","average.between", "wb.ratio","dunn2","avg.silwidth") clust.size <- c("cluster.size") stats.names <- c() row.clust <- c() output.stats <- matrix(ncol = k, nrow = length(clust.assess)) cluster.sizes <- matrix(ncol = k, nrow = k) for(i in c(1:k)){ row.clust[i] <- paste("Cluster-", i, " size") } for(i in c(2:k)){ stats.names[i] <- paste("Test", i-1) for(j in seq_along(clust.assess)){ output.stats[j, i] <- unlist(cluster.stats(d = dist, clustering = cutree(tree, k = i))[clust.assess])[j] } for(d in 1:k) { cluster.sizes[d, i] <- unlist(cluster.stats(d = dist, clustering = cutree(tree, k = i))[clust.size])[d] dim(cluster.sizes[d, i]) <- c(length(cluster.sizes[i]), 1) cluster.sizes[d, i] } } output.stats.df <- data.frame(output.stats) cluster.sizes <- data.frame(cluster.sizes) cluster.sizes[is.na(cluster.sizes)] <- 0 rows.all <- c(clust.assess, row.clust) # rownames(output.stats.df) <- clust.assess output <- rbind(output.stats.df, cluster.sizes)[ ,-1] colnames(output) <- stats.names[2:k] rownames(output) <- rows.all is.num <- sapply(output, is.numeric) output[is.num] <- lapply(output[is.num], round, 2) output } # I am capping the maximum amout of clusters by 7 # but for sure, we can do more # I want to choose a reasonable number, based on which I will be able to see basic differences between customer groups stats.df.divisive <- cstats.table(gower.dist, divisive.clust, 10) stats.df.divisive stats.df.aggl <- cstats.table(gower.dist, aggl.clust.c, 10) stats.df.aggl # --------- Choosing the number of clusters ---------# # Using "Elbow" and "Silhouette" methods to identify the best number of clusters library(ggplot2) # Elbow # Divisive clustering ggplot(data = data.frame(t(stats.df.divisive)), aes(x=cluster.number, y=within.cluster.ss)) + geom_point()+ geom_line()+ ggtitle("") + labs(x = "Num.of clusters", y = "Within sum of squares") + theme(plot.title = element_text(hjust = 0.5)) + theme_bw(base_size=20) # Silhouette ggplot(data = data.frame(t(stats.df.divisive)), aes(x=cluster.number, y=avg.silwidth)) + geom_point()+ geom_line()+ ggtitle("Divisive clustering") + labs(x = "Num.of clusters", y = "Average silhouette width") + theme(plot.title = element_text(hjust = 0.5)) # Agglomorative clustering # Elbow ggplot(data = data.frame(t(stats.df.aggl)), aes(x=cluster.number, y=within.cluster.ss)) + geom_point()+ geom_line()+ ggtitle("") + labs(x = "Num.of clusters", y = "Within sum of squares") + theme(plot.title = element_text(hjust = 0.5)) + theme_bw(base_size=20) # Silhouette ggplot(data = data.frame(t(stats.df.aggl)), aes(x=cluster.number, y=avg.silwidth)) + geom_point()+ geom_line()+ ggtitle("Agglomorative clustering") + labs(x = "Num.of clusters", y = "Average silhouette width") + theme(plot.title = element_text(hjust = 0.5)) # Finally, assigning the cluster number to the observation clust.num <- cutree(divisive.clust, k = 3) id.s = c(1:58) projects.cl <- cbind(id.s, projects, clust.num) clust.aggl.num <- cutree(aggl.clust.c, k = 3) id.s = c(1:58) projects.aggl.cl <- cbind(id.s, projects, clust.aggl.num) #projects.cl <- cbind(projects, clust.num) library("ggplot2") library("reshape2") library("purrr") library("dplyr") library("dendextend") dendro <- as.dendrogram(aggl.clust.c) dendro.col <- dendro %>% set("branches_k_color", k = 3, value = c("gold3", "darkcyan", "cyan3")) %>% set("branches_lwd", 0.6) %>% set("labels_colors", value = c("darkslategray")) %>% set("labels_cex", 0.5) ggd1 <- as.ggdend(dendro.col) ggplot(ggd1, theme = theme_minimal()) + labs(x = "Num. observations", y = "Height", title = "Dendrogram (aggl), k = 3") # Create a radial plot ggplot(ggd1, labels = T) + scale_y_reverse(expand = c(0.2, 0)) + coord_polar(theta="x") # cust.order <- order.dendrogram(dendro) # projects.cl.ord <- projects.cl[cust.order, ] # 1 variable per row # factors have to be converted to characters in order not to be dropped cust.long <- melt(data.frame(lapply(projects.cl, as.character), stringsAsFactors=FALSE), id.vars = c("id.s", "clust.num"), factorsAsStrings=T) cust.aggl.long <- melt(data.frame(lapply(projects.aggl.cl, as.character), stringsAsFactors=FALSE), id.vars = c("id.s", "clust.aggl.num"), factorsAsStrings=T) cust.long.q <- cust.long %>% group_by(clust.num, variable, value) %>% mutate(count = n_distinct(id.s)) %>% distinct(clust.num, variable, value, count) cust.aggl.long.q <- cust.aggl.long %>% group_by(clust.aggl.num, variable, value) %>% mutate(count = n_distinct(id.s)) %>% distinct(clust.aggl.num, variable, value, count) cust.long.p <- cust.long.q %>% group_by(clust.num, variable) %>% mutate(perc = count / sum(count)) %>% arrange(clust.num) cust.aggl.long.p <- cust.aggl.long.q %>% group_by(clust.aggl.num, variable) %>% mutate(perc = count / sum(count)) %>% arrange(clust.aggl.num) heatmap.p <- ggplot(cust.long.p, aes(x = clust.num, y = factor(value, levels = c("1D","+D","+C", "C", "P&S", "I&DM", "OE&A", "L/L","L/H", "H/L", "H/H", "+R","-C","O", "exploit","explore", "I&S","CX","iterate","new"), ordered = T))) + geom_tile(aes(fill = perc), alpha = 0.85)+ labs(title = "Distribution of characteristics across clusters", x = "Cluster number", y = NULL) + geom_hline(yintercept = 3.5) + geom_hline(yintercept = 7.5) + geom_hline(yintercept = 11.5) + geom_hline(yintercept = 14.5) + geom_hline(yintercept = 16.5) + scale_fill_gradient2(low = "darkslategray1", mid = "yellow", high = "turquoise4") heatmap.p heatmap.aggl.p <- ggplot(cust.aggl.long.p, aes(x = clust.aggl.num, y = factor(value, levels = c("1D","+D","+C", "C", "P&S", "I&DM", "OE&A", "L/L","L/H", "H/L", "H/H", "+R","-C","O", "exploit","explore", "I&S","CX","iterate","new"), ordered = T))) + geom_tile(aes(fill = perc), alpha = 0.85)+ labs(title = "Distribution of characteristics across (aggl) clusters", x = "Cluster number", y = NULL) + geom_hline(yintercept = 3.5) + geom_hline(yintercept = 7.5) + geom_hline(yintercept = 11.5) + geom_hline(yintercept = 14.5) + geom_hline(yintercept = 16.5) + scale_fill_gradient2(low = "darkslategray1", mid = "yellow", high = "turquoise4") heatmap.aggl.p

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/EjBrandEval/R/process.Ej.cjs.r

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jlaake/EjBrandEval

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refs/heads/master

2021-01-18T21:56:57.349758

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process.Ej.cjs.r

#' Prepares data for running RMark models #' Prepares data by running process.data and make.design.data step for RMark. #' Creates occasion-specific design data for groups and for occasions based on #' platform used for re-sighting. #' #' @export #' @param ej.list list that results from running \code{\link{extract.Ej}} #' @return \item{data.proc}{Processed data list for RMark} \item{ddl}{Design #' data list for RMark} #' @author Jeff Laake #' @examples #' #' ej.list=extract.Ej() #' ej.list=process.Ej.cjs(ej.list) #' p=vector("list",2) #' p[[1]]=list(formula=~late:Tag:time + time + patch1:Tag + patch2:Tag + patch3:Tag + scope:Tag + camera:Tag) #' p[[2]]=list(formula=~late:Tag + time + patch1:Tag + patch2:Tag + patch3:Tag + scope:Tag + camera:Tag) #' Phi=vector("list",1) #' Phi[[1]]=list(formula=~early:trt+Brand:batch) #' results=run.cjs.models(p,Phi,ej.list) #' process.Ej.cjs <-function(ej.list) { # # Create time interval vector with a unit interval of 7 days # ti=as.numeric(diff(ej.list$times)/7) # # Process data # ej.proc=process.data(ej.list$data,model="CJS",groups=c("trt","tag","experiment","brand","batch","brander","anesthesiologist","sex","treatno"),time.intervals=ti,begin.time=0) # # Create design data # ej.ddl=make.design.data(ej.proc,parameters=list(Phi=list(pim.type="time"),p=list(pim.type="time"))) # # Add early late split on Phi and p # ej.ddl$Phi$early=0 ej.ddl$Phi$early[ej.ddl$Phi$Time<10]=1 ej.ddl$p$late=1 ej.ddl$p$late[ej.ddl$p$Time<11]=0 ej.ddl$p$Brand=as.numeric(as.character(ej.ddl$p$brand)) ej.ddl$p$Tag=1-ej.ddl$p$Brand ej.ddl$Phi$Brand=as.numeric(as.character(ej.ddl$Phi$brand)) ej.ddl$Phi$Tag=1-ej.ddl$Phi$Brand # # Create occasion data frame to merge with the p design data # times=cumsum(ti) # Need to change these for added occasions patch1=c(rep(1,5),rep(0,58)) patch2=c(rep(0,5),rep(1,5),rep(0,53)) patch3=c(rep(0,10),rep(1,5),rep(0,48)) yp=c(rep(1,15),rep(0,48)) camera=ej.list$platform[,1] vessel=ej.list$platform[,2] scope=ej.list$platform[,3] xcov=data.frame(patch1=patch1,patch2=patch2,patch3=patch3,yp=yp,camera=camera,vessel=vessel,scope=scope) if(ej.list$scenario==2)xcov=xcov[1:50,] if(ej.list$scenario==4)xcov=xcov[-c(54,55,57,59),] ej.ddl=merge_design.covariates(ej.ddl,"p",cbind(times=times,xcov)) ej.ddl$p$extra=0 ej.ddl$p$extra[ej.ddl$p$experiment==0]=1 ej.ddl$Phi$threshold1=0 ej.ddl$Phi$threshold1[ej.ddl$Phi$Time<4]=1 ej.ddl$Phi$threshold2=0 ej.ddl$Phi$threshold2[ej.ddl$Phi$Time>=4&ej.ddl$Phi$Time<8]=1 ej.ddl$Phi$threshold3=0 ej.ddl$Phi$threshold3[ej.ddl$Phi$Time>=4&ej.ddl$Phi$Time<6]=1 ej.ddl$Phi$threshold4=0 ej.ddl$Phi$threshold4[ej.ddl$Phi$Time>=6&ej.ddl$Phi$Time<8]=1 ej.ddl$Phi$threshold5=0 ej.ddl$Phi$threshold5[ej.ddl$Phi$Time>=8&ej.ddl$Phi$Time<10]=1 return(list(data.proc=ej.proc,ddl=ej.ddl)) }

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/R/get.index.mat.R

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kellijohnson-NOAA/saconvert

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get.index.mat.R

#' #' get.index.mat<- function(x, cv, neff, first.year, nyears, catch.ages, survey.ages) { n.ages = length(catch.ages) last.yr <- first.year+nyears - 1 tmp.yrs <- as.numeric(rownames(x)) all.years = first.year-1 + 1:nyears years.use.ind = which(tmp.yrs %in% all.years) #if (tmp.yrs[length(tmp.yrs)]>last.yr) tmp.yrs <- tmp.yrs[-which(tmp.yrs>last.yr)] tmp.ages <- as.numeric(colnames(x)) tmp.ages = catch.ages survey.ages.index = which(catch.ages %in% survey.ages) i.mat <- matrix(0, nyears, (n.ages + 4)) i.mat[,1] <- all.years rownames(x) <- c() colnames(x) <- c() x[is.na(x)] <- 0 print(dim(x)) print(dim(i.mat)) print(survey.ages.index) print(tmp.yrs) print(sum(all.years %in% tmp.yrs)) print(all.years) print(x) tmp.ind.total <- apply(x[years.use.ind,], 1, sum) i.mat.ind = which(all.years %in% tmp.yrs[years.use.ind]) i.mat[i.mat.ind,2:3] <- cbind(tmp.ind.total, rep(cv, length(years.use.ind))) i.mat[i.mat.ind, (3+survey.ages.index)] <- x[years.use.ind,] i.mat[i.mat.ind, (n.ages+4)] <- rep(neff, length(years.use.ind)) return(i.mat) }

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/code/part_3.R

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ewong027/stats133-final-project

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part_3.R

# ====================================================================== # Part 3: Type analysis # Description: Here we are looking at how the most common types of # airplanes to crash changed over different decades. # ====================================================================== # Note: must have ran part_2 first. ## ---- Preliminary ---- # packages needed library(stringr) library(ggplot2) # functions needed source('../code/fun_top3.R') ## ---- comment ---- # Here we are parsing out the data by decade so that we can plot by type and # extracting just the top three values. ## ---- Top3 Array ---- top3_array <- fun_top3(data, decade_names) ## ---- comment ---- # Now we want to create a data frame that combines the data from each # decade in a way that we can plot it efficiently. ## ---- Reorganizing ---- type <- names(top3_array) freq <- as.vector(top3_array) decade <- rep(decade_names, each = 3) top3 <- data.frame(type = type, freq = freq, decade = decade) ## ---- comment ---- # These are the two sets of decades we want to focus on. # Because of the differences among our data sets, we want to look at: # - from 1940-1980 # - from 1980-2010 ## ---- Parsing: 1940-1980 ---- decade_names_1 <- decade_names[3:6] type_1 <- type[7:18] freq_1 <- freq[7:18] decade_1 <- rep(decade_names_1, each = 3) top3_1 <- data.frame(type = type_1, freq = freq_1, decade = decade_1) # plotting the trend ggplot(top3_1, aes(x = decade, y = freq, fill = type))+ geom_bar( stat = 'identity', position = position_dodge())+ ggtitle('Most Common Planes in Accidents 1940s to 1970s')+ theme(plot.title = element_text(size = rel(.75))) ## ---- Parsing: 1980-2010 ---- decade_names_2 <- decade_names[7:9] type_2 <- type[19:27] freq_2 <- freq[19:27] decade_2 <- rep(decade_names_2, each = 3) top3_2 <- data.frame(type = type_2, freq = freq_2, decade = decade_2) # plotting the trend ggplot(top3_2, aes(x = decade, y = freq, fill = type))+ geom_bar( stat = 'identity', position = position_dodge())+ ggtitle('Most Common Planes in Accidents 1980s to 2000s') ## ---- Exporting the Graphics ---- # Exporting the Graphics # PDF pdf('../plots_and_graphics/most_common_planes_in_accidents_1940s_to_1970s.pdf') ggplot(top3_1, aes(x = decade, y = freq, fill = type))+ geom_bar( stat = 'identity', position = position_dodge())+ ggtitle('Top 3 Planes in Accidents 1940s to 1970s') dev.off() pdf('../plots_and_graphics/most_common_planes_in_accidents_1980s_to_2000s.pdf') ggplot(top3_2, aes(x = decade, y = freq, fill = type))+ geom_bar( stat = 'identity', position = position_dodge())+ ggtitle('Top 3 Planes in Accidents 1980s to 2000s') dev.off() # PNG png('../plots_and_graphics/most_common_planes_in_accidents_1940s_to_1970s.png', res = 96, width = 700, height = 500) ggplot(top3_1, aes(x = decade, y = freq, fill = type))+ geom_bar( stat = 'identity', position = position_dodge())+ ggtitle('Top 3 Planes in Accidents 1940s to 1970s') dev.off() png('../plots_and_graphics/most_common_planes_in_accidents_1980s_to_2000s.png', res = 96) ggplot(top3_2, aes(x = decade, y = freq, fill = type))+ geom_bar( stat = 'identity', position = position_dodge())+ ggtitle('Top 3 Planes in Accidents 1980s to 2000s') dev.off()

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/man/taxa_rollup.Rd

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pmartR/pmartRseq

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taxa_rollup.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/taxa_rollup.R \name{taxa_rollup} \alias{taxa_rollup} \title{Roll up data to a specified taxonomic level} \usage{ taxa_rollup(omicsData, level, taxa_levels = NULL) } \arguments{ \item{omicsData}{an object of the class 'seqData' created by \code{\link{as.seqData}}.} \item{level}{taxonomic level to roll up to.} \item{taxa_levels}{The levels of taxonomy (or other e_meta object) which might be used in the roll up. If NULL, will use c("Kingdom","Phylum","Class","Order","Family","Genus","Species"), in that order. Default is NULL.} } \value{ A seqData object of the same class as the input, where e_data and e_meta are rolled up to the specified level. } \description{ This function rolls up data to the specified taxonomic level so that statistics may be calculated at various taxonomic levels. } \details{ Data will be rolled (summed) up to a specified taxonomic level. For example, data at the OTU level could be rolled (summed) up to the Genus level before statistics are computed. } \examples{ \dontrun{ library(mintJansson) data(rRNA_data) rRNA_split <- split_emeta(rRNA_data) rRNA_rollup <- taxa_rollup(omicsData = rRNA_split, level = "Phylum") dim(rRNA_rollup$e_data) attributes(rRNA_rollup) } } \author{ Allison Thompson }

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/plot1.R

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meganminshew/ExData_Plotting1

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plot1.R

## read the data from the working folder dt <- read.delim("household_power_consumption.txt", header = TRUE, sep = ";", colClasses="character") ## class the date dt$Date = as.Date(dt$Date, format = '%d/%m/%Y') ## filter for the 2 days dt <- subset(dt, dt$Date == "2007-02-01" | dt$Date == "2007-02-02") ## get the attribute to plot and make it a number gap <- as.numeric(dt$Global_active_power) ##output the histogram to PNG png(filename="plot1.png", width=480, height=480, units="px", bg="transparent") hist(gap, col="red", main="Global Active Power", xlab="Global Active Power (kilowatts)") dev.off()

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/supervised/generative_learning/naive_bayes/golf_play.R

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sureshpodeti/Machine-Learning

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golf_play.R

# Load the data into data frame df <- read.csv('/home/podeti/Desktop/AI/Machine-Learning/Data/golf_play.csv') n <- ncol(df) # convert dataframe into matrix data <- data.matrix(df) # split the data into train and test data library(caret) partitionIndex <- createDataPartition(data[, n], p=0.7, list=FALSE) data_train <- data[partitionIndex, ] data_test <- data[-partitionIndex, ] data_train_y_0 <- data_train[data_train[, n] == 0, ] data_train_y_1 <- data_train[data_train[,n] == 1, ] data_train_features_y_0 <- data_train_y_0[, -n] data_train_target_y_0 <- data_train_y_0[, n] data_train_features_y_1 <- data_train_y_1[, -n] data_train_target_y_1 <- data_train_y_1[, n]

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cran/dynatopmodel

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refs/heads/master

2021-01-21T21:54:23.182728

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get.run.par.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/defs.r \name{get.run.par} \alias{get.run.par} \title{get.run.par Initalise model run parameters. Note this function is maintained for backward compatibility only} \usage{ get.run.par(tms = NULL, dt = NULL, units = "secs", ...) } \arguments{ \item{tms}{xts time series or an object that has a POSIXct index} \item{dt}{Numeric Time step in seconds} \item{units}{string Units for dt.} \item{...}{Any other parameters returned by get.run.par} } \value{ Structure to maintain run information. } \description{ get.run.par Initalise model run parameters. Note this function is maintained for backward compatibility only } \details{ The returned value includes a simulation times calculated from the supplied time range and interval }

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kleinschmidt/brewnotes

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alt-as-sin-2016.R

library(magrittr) devtools::load_all() brewnotes::strike_decoc_topoff_sparge_gal(grain_lbs = 10.5, mash_thickness = 1.5) %T>% print() %>% brewnotes::gal_to_lbs() %>% `+`(2.3) # tare weight on bucket

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/plot2.R

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dvsdimas/exploratory-analysis-project-2

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2016-12-12T17:50:44

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plot2.R

require(dplyr) get_data <- function() { data_folder <- file.path(getwd(), "data") file_source <- file.path(data_folder, "Source_Classification_Code.rds") file_summary <- file.path(data_folder, "summarySCC_PM25.rds") if(!dir.exists(data_folder)) { dir.create(data_folder) if(!dir.exists(data_folder)){ stop(paste0("Cannot create folder ", data_folder)) } } if(!file.exists(file_source) || !file.exists(file_summary)) { zip_file <- file.path(data_folder, "exdata.zip") if(!file.exists(zip_file)) { data_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" download.file(data_url, destfile = zip_file) if(!file.exists(zip_file)) { stop(paste0("Cannot download zip file : ", data_url)) } } unzip(zip_file, exdir = data_folder) if(!file.exists(file_source) || !file.exists(file_summary)) { stop(paste0("Cannot unpack zip file : ", zip_file)) } unlink(zip_file) } NEI <- readRDS(file_summary) if( (dim(NEI)[1] != 6497651) || (dim(NEI)[2] != 6) ) { stop("NEI data has wrong format") } SCC <- readRDS(file_source) if( (dim(SCC)[1] != 11717) || (dim(SCC)[2] != 15) ) { stop("SCC data has wrong format") } list(NEI = NEI, SCC = SCC) } ret <<- NULL get_cdata <- function() { if(!is.null(ret)) { return(ret) } ret <<- get_data() } # Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (fips == "24510") from 1999 to 2008? # Use the base plotting system to make a plot answering this question. D <- get_cdata() trend <- D$NEI %>% filter(fips == "24510") %>% group_by(year) %>% summarise(mean = mean(Emissions, na.rm = TRUE)) png("plot2.png", width = 480, height = 480) plot(trend$year, trend$mean, main = "Total emissions PM2.5 in the Baltimore City, MD", ylab = "PM2.5, tons", xlab = "Year", type = "l") dev.off()

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library(LilRhino) ### Name: Codes_done ### Title: For announcing when code is done. ### Aliases: Codes_done ### ** Examples Codes_done("done", "check it", sound = TRUE, effect = 1)

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library(htmlTable) outdir.tables = "G:/mydocuments/SDSU/research/CA/ET_MOD16_SEBAL_towers/writeups/tables/" sourcedir = "G:/mydocuments/SDSU/research/CA/ET_MOD16_SEBAL_towers/Rfiles/plots/" sourcefile = "plot_ts_PET_MOD16_tower_multiple_in_one.R" names(statsout2) = c("Tower","MOD16","Error %") outhtml = htmlTable(statsout2, rowlabel = "Tower name", cgroup = c("ETo",""), n.cgroup = c(2,1), caption = "Table 3. Comparison of mean seasonal reference evapotranspiration (ETo) from MOD16 and towers.", ) setwd(outdir.tables) sink("Table_xx_ETo_tower_MOD16_error.html") print(outhtml,type="html",useViewer=FALSE) sink()

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\name{rattle.print.summary.multinom} \alias{rattle.print.summary.multinom} \title{ Print information about a multinomial model } \description{ Displays a textual reveiw of the performance of a multinom model. } \usage{ rattle.print.summary.multinom(x, digits = x$digits, ...) } \arguments{ \item{x}{An rpart object.} \item{digits}{Number of digist to print for numbers.} \item{...}{Other arguments.} } \details{ Print a summary of a multinom model. This is sipmly a modification of the print.summary.multinom function to add the number of entities! } \references{Package home page: \url{https://rattle.togaware.com}} \author{\email{Graham.Williams@togaware.com}}

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# oneHot.R # ::rtemis:: # 2019 E.D. Gennatas lambdamd.org #' One hot encoding #' #' One hot encode a vector or factors in a data.frame #' #' A vector input will be one-hot encoded regardless of type by looking at all unique values. With data.frame input, #' only column of type factor will be one-hot encoded. This function is used by \link{preprocess} #' @param x Vector or data.frame #' @param verbose Logical: If TRUE, print messages to console. Default = TRUE #' @return For vector input, a one-hot-encoded matrix, for data.frame frame input, an expanded data.frame where all #' factors are one-hot encoded #' @author E.D. Gennatas #' @export oneHot <- function(x, verbose = FALSE) { UseMethod("oneHot", x) } # rtemis::oneHot

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plot4.R

#read data. values "?" are intrepreted as NA. file household_power_consumption.txt should be in the working directory raw <- read.csv("household_power_consumption.txt", sep=";", na.strings = c("?")) #remove NA from data clean <- na.omit(raw) #convert Date and Time from character vector to date and time clean$Time = strptime(paste(clean$Date, clean$Time), format = "%d/%m/%Y %H:%M:%S") clean$Date <- as.Date(clean$Date, format="%d/%m/%Y") #subset only relevant dates data <- subset(clean, Date >= "2007-02-01" & Date <= "2007-02-02") #plot directly to png. When copying from screen, some parts of graph are missing png("plot4.png") #set canvas to 2x2 par(mfcol = c(2,2)) #plot first graph with(data, plot(Time, Global_active_power, type="n", xlab = "", ylab = "Global Active Power")) with(data, lines(Time, Global_active_power)) #plot second graph with(data, plot(Time, Sub_metering_1, type="n", xlab = "", ylab = "Energy sub metering")) with(data, lines(Time, Sub_metering_1)) with(data, lines(Time, Sub_metering_2, col = "red")) with(data, lines(Time, Sub_metering_3, col = "blue")) #add legend legend("topright", bty = "n", legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), col = c("black", "red", "blue"), lty = c(1,1,1)) #plot third graph with(data, plot(Time, Voltage, xlab = "datetime", ylab = "Voltage", type = "n")) with(data, lines(Time, Voltage)) #plot fourth graph with(data, plot(Time, Global_reactive_power, xlab = "datetime", type = "n")) with(data, lines(Time, Global_reactive_power)) #close device dev.off()

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/CreateFlowChart.R \name{CreateFlowChart} \alias{CreateFlowChart} \title{'CreateFlowChart'} \usage{ CreateFlowChart(dataset, listcriteria, weight, strata, flowchartname) } \arguments{ \item{dataset}{input dataset to work with} \item{listcriteria}{list of boolean/binary variables} \item{weight}{(optional) weight variable: in the input dataset each row may represent multiple unit of observations, if this is the case weight contains the weight of each row} \item{strata}{(optional) categorical variable representing strata} \item{flowchartname:}{filename (possibly with path) of the output dataset containing the flowchart} } \description{ CreateFlowChart takes as input a dataset where a list of exclusion criteria is represented as binary or boolean variables. The output are two datasets (a) the input dataset itself, restricted to the sole rows which don't match any exclusion criterion; this dataset is returned at the end of the function, and (b) the flowchart representing how many units were discarded by each criterion; this dataset is saved in the R environment. Criteria are considered to be hierarchical. As an option, the count is performed across strata of a categorical variable. }

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hex <- function(point, coord) { diff <- list( nw = list(d = c(1, -1), link = 'n'), n = list(d = c(1, 1), link = 'ne'), ne = list(d = c(0, 1), link = 'se'), se = list(d = c(-1, 1), link = 's'), s = list(d = c(-1, -1), link = 'sw'), sw = list(d = c(0, -1), link = 'nw') ) res <- setNames(list(coord), point) for (i_ in seq_along(diff)) { x <- diff[[point]] coord <- coord + x$d point <- x$link res[[point]] <- coord } list( w = res[c('sw', 'nw')], nw = res[c('nw', 'n')], ne = res[c('n', 'ne')], e = res[c('se', 'ne')], se = res[c('s', 'se')], sw = res[c('sw', 's')] ) } hex_links <- function() list( w = 'e', e = 'w', nw = 'se', se = 'nw', ne = 'sw', sw = 'ne' ) hex_path <- function(x) { i <- 1 links <- hex_links() sides <- names(links) res <- list(h <- hex('nw', c(0, 0))) repeat { if (i > length(x)) break() p <- x[i] if (!p %in% sides) { p <- paste0(p, x[i + 1]) i <- i + 1 } side <- h[[p]] adj_points <- names(h[[links[[p]]]]) adj_side <- setNames(side, adj_points) h <- hex(names(adj_side)[1], adj_side[[1]]) res[[length(res) + 1]] <- h i <- i + 1 } res } # l <- readLines('data/day24_example.txt', warn = FALSE) # l <- strsplit(l, '') l <- readLines('data/day24.txt', warn = FALSE) l <- strsplit(l, '') md5 <- digest::getVDigest() tiles <- vapply(l, function(x) { p <- hex_path(x) md5(p[length(p)]) }, character(1)) f <- table(tiles) print(length(f[f == 1])) # part two .h <- function(x) paste0('hex_', md5(x)) set_names <- function(tiles) vapply(tiles, function(x) .h(list(x)), character(1)) is_white <- function(n) n %% 2 == 0 is_black <- function(n) ! is_white(n) adj_hex <- function(x) { links <- hex_links() o <- lapply(seq_along(x), function(i) { side <- names(x)[i] adj_points <- names(x[[links[[side]]]]) adj_side <- setNames(x[[i]], adj_points) hex(names(adj_side)[1], adj_side[[1]]) }) names(o) <- set_names(o) o } update_tiles <- function(o) { black_tiles <- o$black_tiles adj <- o$adj if (is.null(adj)) adj <- lapply(black_tiles, adj_hex) nms <- names(black_tiles) res <- res_adj <- white_tiles <- list() for (i in seq_along(black_tiles)) { x <- nms[i] x.adj <- names(a <- adj[[x]]) n.b <- length(x.adj[x.adj %in% nms]) a.w <- x.adj[!x.adj %in% nms] white_tiles[a.w] <- a[a.w] if (n.b <= 2 && n.b != 0) { res[[x]] <- black_tiles[[x]] res_adj[[x]] <- a } } white_tiles <- white_tiles[unique(names(white_tiles))] for (i in seq_along(white_tiles)) { x.tile <- white_tiles[[i]] x <- names(white_tiles)[i] x.adj <- names(a <- adj_hex(x.tile)) n.b <- length(x.adj[x.adj %in% nms]) if (n.b == 2) { res[[x]] <- x.tile res_adj[[x]] <- a } } list(black_tiles = res, adj = res_adj) } run_days <- function(tiles, n = 100) { colors <- table(names(tiles)) black <- colors[is_black(colors)] tiles <- tiles[names(black)] o <- list(black_tiles = tiles, adj = NULL) for (i_ in 1:n) { o <- update_tiles(o) cat(i_, ':', length(o[[1]]), '\n') } invisible(o[[1]]) } # l <- readLines('data/day24_example.txt', warn = FALSE) # l <- strsplit(l, '') l <- readLines('data/day24.txt', warn = FALSE) l <- strsplit(l, '') tiles <- lapply(l, function(x) { p <- hex_path(x) p[[length(p)]] }) names(tiles) <- set_names(tiles) res <- run_days(tiles, n = 100) print(length(res)) # 3937

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#' Fitting options for a new Vine Layer #' #' When fitting a new layer(s) to a vine, use this function to specify #' "known" components of the new layer(s), as well as #' #' @param ntrunc Truncation level. Could be a vector corresponding to the #' truncation level for the variables \code{var} or \code{G[1, ]}. #' @note The arrays here use the newer form, where variables go in row 1. #' @return A list of partially-specified layers of a vine. #' #' Regarding the vine array info: #' #' \itemize{ #' \item \code{$var} Vector of new variables that these layers add, not #' necessarily in order. #' \item \code{$ntrunc} Depending on how much information is input, could be #' \code{NULL}, an integer for maximum tree depth of these layers, or a #' vector of tree depth for each layer. #' \item \code{$G} Either \code{NULL}, or a vine array. #' } #' #' Regarding copula and parameter info: #' #' \itemize{ #' \item \code{$copmat} Copula matrix for these layers. No blank column #' to the left. Some entries may be \code{NA}. #' \item \code{$cparmat} Copula parameter matrix for these layers. No #' blank column to the left. Some entries may contain \code{NA}'s. #' } #' @export layeropts <- function(var=NULL, G=NULL, ntrunc=NULL, cops=NULL, cpar=NULL, families = c("bvncop","bvtcop","mtcj","gum","frk", "joe","bb1","bb7","bb8")){ ## Deal with array-related things first. if (is.null(G) & is.null(var)) stop("At least one of 'var' or 'G' must be specified.") if (!is.null(G) & !is.null(var)) { warning("Both 'var' and 'G' are specified -- ignoring 'var'.") var <- G[1, ] } if (is.null(G)) { # In this case, var is specified, G is not. ## Check that the var input contains integers. if (any(is.na(var)) | any(!is.numeric(var))) stop("'var' must be entirely integers, containing no NA's.") if (any(var%%1!=0)) # Check that they're integers. is.integer won't work. stop("'var' must be entirely integers, containing no NA's.") } if (is.null(var)) { # In this case, G is specified, var is not. var <- G[1, ] } list(var=var, G=G, ntrunc=ntrunc, copmat=cops, cparmat=cpar, families=families) }

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#load libraries library(tidyverse) library(knitr) library(kableExtra) library(gridExtra) theme_set(theme_bw()) #import quartet data set quartet <- read.csv("Data/Quartet.csv") #summarize data by mean, median, standard deviation and show in table colNames <- c("X1", "Y1", "X2", "Y2", "X3", "Y3", "X4", "Y4") quartetMean <- apply(quartet, 2, mean) quartetMedian <- apply(quartet, 2, median) quartetSD <- apply(quartet, 2, sd) summaryTable <- tibble(colNames, quartetMean, quartetMedian, quartetSD) %>% rename(Column = colNames, "Sample Mean" = quartetMean, "Sample Median" = quartetMedian, "Sample Standard Deviation" = quartetSD) %>% mutate_if(is.numeric, round, digits = 2) kable(summaryTable) %>% kable_styling(bootstrap_options = c("striped", "hover")) #plot all four paired data sets to compare to summary statistics p1 <- ggplot(data = quartet, aes(x = x1, y = y1)) + geom_point(size = 3) + xlim(0, 19) + ylim(0, 15) p2 <- ggplot(data = quartet, aes(x = x2, y = y2)) + geom_point(size = 3) + xlim(0, 19) + ylim(0, 15) p3 <- ggplot(data = quartet, aes(x = x3, y = y3)) + geom_point(size = 3) + xlim(0, 19) + ylim(0, 15) p4 <- ggplot(data = quartet, aes(x = x4, y = y4)) + geom_point(size = 3) + xlim(0, 19) + ylim(0, 15) grid.arrange(p1, p2, p3, p4)

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# The director of a prison offers 100 death row prisoners, # who are numbered from 1 to 100, a last chance. A room # contains a cupboard with 100 drawers. The director randomly # puts one prisoner's number in each closed drawer. The prisoners # enter the room, one after another. Each prisoner may open and # look into 50 drawers in any order. The drawers are closed again # afterwards. If, during this search, every prisoner finds their # number in one of the drawers, all prisoners are pardoned. If even # one prisoner does not find their number, all prisoners die. Before #the first prisoner enters the room, the prisoners may discuss # strategy — but may not communicate once the first prisoner enters to # look in the drawers. What is the prisoners' best strategy? # This function takes the list of drawers and the current prisoner number. # It then follows the strategy to check the drawers. prisonerStrategy <- function(listOfDrawers, currentPrisonerNumber) { nextDrawerInSequence <- listOfDrawers[currentPrisonerNumber] for(prisonerNumber in 1:50) { if(nextDrawerInSequence == currentPrisonerNumber) { return(TRUE) } nextDrawerInSequence <- listOfDrawers[nextDrawerInSequence] } return(FALSE) } listOfPrisoners <- 1:100 # simCount controls how many times we see if all the prisoners live or die # Set this value lower for a quicker response, and higher for more accuracy simCount <- 100000 listOfSimulationResults <- rep(0, simCount) for(simulationIteration in 1:simCount) { drawers <- sample(c(1:100), 100) LiveOrDie <- rep(0, 100) for(prisonerNumber in listOfPrisoners) { if(prisonerStrategy(drawers,prisonerNumber)) { LiveOrDie[prisonerNumber] <- 1 } } if(sum(LiveOrDie) == 100) { listOfSimulationResults[simulationIteration] <- 1 } } # This prints the average of the simulation results # The higher the number of simulation runs the closer # to ~0.31183 the value should get print(mean(listOfSimulationResults))

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test-jit-ops.R

test_that("can access operators via ops object", { # matmul, default use res <- jit_ops$aten$matmul(torch::torch_ones(5, 4), torch::torch_rand(4, 5)) expect_equal(dim(res), c(5, 5)) # matmul, passing out tensor t1 <- torch::torch_ones(4, 4) t2 <- torch::torch_eye(4) out <- torch::torch_zeros(4, 4) jit_ops$aten$matmul(t1, t2, out) expect_equal_to_tensor(t1, out) # split, returning two tensors in a list of length 2 res_torch <- torch_split(torch::torch_arange(0, 3), 2, 1) res_jit <- jit_ops$aten$split(torch::torch_arange(0, 3), torch::jit_scalar(2L), torch::jit_scalar(0L)) expect_length(res_jit, 2) expect_equal_to_tensor(res_jit[[1]], res_torch[[1]]) expect_equal_to_tensor(res_jit[[2]], res_torch[[2]]) # split, returning a single tensor res_torch <- torch_split(torch::torch_arange(0, 3), 4, 1) res_jit <- jit_ops$aten$split(torch::torch_arange(0, 3), torch::jit_scalar(4L), torch::jit_scalar(0L)) expect_length(res_jit, 1) expect_equal_to_tensor(res_jit[[1]], res_torch[[1]]) # linalg_qr always returns a list m <- torch_eye(5)/5 res_torch <- linalg_qr(m) res_jit <- jit_ops$aten$linalg_qr(m, torch::jit_scalar("reduced")) expect_equal_to_tensor(res_torch[[2]], res_jit[[2]]) }) test_that("can print ops objects at different levels", { local_edition(3) expect_snapshot(jit_ops) expect_snapshot(jit_ops$sparse) expect_snapshot(jit_ops$prim$ChunkSizes) expect_snapshot(jit_ops$aten$fft_fft) })

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/R/testing_fun.R

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[]

no_license

felix28dls/ddCt_QPCR_Analysis

b052a4beb308e7650cda03f864829e6266c1ca20

0539f31edb2b3b506de0a9c2db90a01190fb0e3c

refs/heads/master

2020-06-16T20:55:48.974964

2019-07-07T22:28:42

195,700,929

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11,266

r

testing_fun.R

#' Statistical testing of PCR data #' #' A unified interface to different statistical significance tests for qPCR data #' #' @inheritParams pcr_ddct #' @param test A character string; 't.test' default, 'wilcox.test' or 'lm' #' @param ... Other arguments for the testing methods #' #' @return A data.frame of 5 columns in addition to term when test == 'lm' #' \itemize{ #' \item term The linear regression comparison terms #' \item gene The column names of df. reference_gene is dropped #' \item estimate The estimate for each term #' \item p_value The p-value for each term #' \item lower The low 95\% confidence interval #' \item upper The high 95\% confidence interval #' } #' For details about the test methods themselves and different parameters, #' consult \code{\link[stats]{t.test}}, \code{\link[stats]{wilcox.test}} #' and \code{\link[stats]{lm}} #' #' @details The simple t-test can be used to test the significance of the #' difference between two conditions \eqn{\Delta C_T}. t-test assumes in addition, #' that the input \eqn{C_T} values are normally distributed and the variance #' between conditions are comparable. #' Wilcoxon test can be used when sample size is small and those two last #' assumptions are hard to achieve. #' #' Two use the linear regression here. A null hypothesis is formulated as following, #' \deqn{ #' C_{T, target, treatment} - C_{T, control, treatment} = #' C_{T, target, control} - C_{T, control, control} #' \quad \textrm{or} \quad \Delta\Delta C_T #' } #' This is exactly the \eqn{\Delta\Delta C_T} as explained earlier. So the #' \eqn{\Delta\Delta C_T} is estimated and the null is rejected when #' \eqn{\Delta\Delta C_T \ne 0}. #' #' @references Yuan, Joshua S, Ann Reed, Feng Chen, and Neal Stewart. 2006. #' “Statistical Analysis of Real-Time PCR Data.” BMC Bioinformatics 7 (85). #' BioMed Central. doi:10.1186/1471-2105-7-85. #' #' @examples #' # locate and read data #' fl <- system.file('extdata', 'ct4.csv', package = 'pcr') #' ct4 <- readr::read_csv(fl) #' #' # make group variable #' group <- rep(c('control', 'treatment'), each = 12) #' #' # test using t-test #' pcr_test(ct4, #' group_var = group, #' reference_gene = 'ref', #' reference_group = 'control', #' test = 't.test') #' #' # test using wilcox.test #' pcr_test(ct4, #' group_var = group, #' reference_gene = 'ref', #' reference_group = 'control', #' test = 'wilcox.test') #' #' # testing using lm #' pcr_test(ct4, #' group_var = group, #' reference_gene = 'ref', #' reference_group = 'control', #' test = 'lm') #' #' # testing advanced designs using a model matrix #' # make a model matrix #' group <- relevel(factor(group), ref = 'control') #' dose <- rep(c(100, 80, 60, 40), each = 3, times = 2) #' mm <- model.matrix(~group:dose, data = data.frame(group, dose)) #' #' # test using lm #' pcr_test(ct4, #' reference_gene = 'ref', #' model_matrix = mm, #' test = 'lm') #' #' # using linear models to check the effect of RNA quality #' # make a model matrix #' group <- relevel(factor(group), ref = 'control') #' set.seed(1234) #' quality <- scale(rnorm(n = 24, mean = 1.9, sd = .1)) #' mm <- model.matrix(~group + group:quality, data = data.frame(group, quality)) #' #' # testing using lm #' pcr_test(ct4, #' reference_gene = 'ref', #' model_matrix = mm, #' test = 'lm') #' #' # using linear model to check the effects of mixing separate runs #' # make a model matrix #' group <- relevel(factor(group), ref = 'control') #' run <- factor(rep(c(1:3), 8)) #' mm <- model.matrix(~group + group:run, data = data.frame(group, run)) #' #' # test using lm #' pcr_test(ct4, #' reference_gene = 'ref', #' model_matrix = mm, #' test = 'lm') #' #' @export pcr_test <- function(df, test = 't.test', ...) { switch (test, 't.test' = pcr_ttest(df, ...), 'wilcox.test' = pcr_wilcox(df, ...), 'lm' = pcr_lm(df, ...) ) } #' t-test qPCR data #' #' @inheritParams pcr_ddct #' @param tidy A \code{logical} whether to return a \code{list} of \code{htest} #' or a tidy \code{data.frame}. Default TRUE. #' @param ... Other arguments to \code{\link[stats]{t.test}} #' #' @return A data.frame of 5 columns #' \itemize{ #' \item gene The column names of df. reference_gene is dropped #' \item estimate The estimate for each term #' \item p_value The p-value for each term #' \item lower The low 95\% confidence interval #' \item upper The high 95\% confidence interval #' } #' When \code{tidy} is FALSE, returns a \code{list} of \code{htest} objects. #' #' @examples #' # locate and read data #' fl <- system.file('extdata', 'ct4.csv', package = 'pcr') #' ct4 <- readr::read_csv(fl) #' #' # make group variable #' group <- rep(c('control', 'treatment'), each = 12) #' #' # test #' pcr_ttest(ct4, #' group_var = group, #' reference_gene = 'ref', #' reference_group = 'control') #' #' # test using t.test method #' pcr_test(ct4, #' group_var = group, #' reference_gene = 'ref', #' reference_group = 'control', #' test = 't.test') #' #' @importFrom purrr map #' @importFrom stats t.test relevel #' @importFrom dplyr data_frame bind_rows #' #' @export pcr_ttest <- function(df, group_var, reference_gene, reference_group, tidy = TRUE, ...) { # calculate the delta_ct values norm <- .pcr_normalize(df, reference_gene = reference_gene) # adjust the reference group group_levels <- unique(group_var) if(length(group_levels) != 2) { stop('t.test is only applied to two group comparisons.') } ref <- group_levels[group_levels != reference_group] group_var <- relevel(factor(group_var), ref = ref) # perform test tst <- map(norm, function(x) { t.test(x ~ group_var, ...) }) # make a tidy data.frame or return htest object if(tidy) { res <- bind_rows(map(tst, function(x) { data_frame( estimate = unname(x$estimate[1] - x$estimate[2]), p_value = x$p.value, lower = x$conf.int[1], upper = x$conf.int[2] ) }), .id = 'gene') } else { res <- tst } # return return(res) } #' Wilcoxon test qPCR data #' #' @inheritParams pcr_ddct #' @param tidy A \code{logical} whether to return a \code{list} of \code{htest} #' or a tidy \code{data.frame}. Default TRUE. #' @param ... Other arguments to \code{\link[stats]{wilcox.test}} #' #' @return A data.frame of 5 columns #' \itemize{ #' \item gene The column names of df. reference_gene is dropped #' \item estimate The estimate for each term #' \item p_value The p-value for each term #' \item lower The low 95\% confidence interval #' \item upper The high 95\% confidence interval #' } #' #' When \code{tidy} is FALSE, returns a \code{list} of \code{htest} objects. #' #' @examples #' # locate and read data #' fl <- system.file('extdata', 'ct4.csv', package = 'pcr') #' ct4 <- readr::read_csv(fl) #' #' # make group variable #' group <- rep(c('control', 'treatment'), each = 12) #' #' # test #' pcr_wilcox(ct4, #' group_var = group, #' reference_gene = 'ref', #' reference_group = 'control') #' #' # test using wilcox.test method #' pcr_test(ct4, #' group_var = group, #' reference_gene = 'ref', #' reference_group = 'control', #' test = 'wilcox.test') #' #' @importFrom purrr map #' @importFrom stats wilcox.test relevel #' @importFrom dplyr data_frame bind_rows #' #' @export pcr_wilcox <- function(df, group_var, reference_gene, reference_group, tidy = TRUE, ...) { # calculate the delta_ct values norm <- .pcr_normalize(df, reference_gene = reference_gene) # adjust the reference group group_levels <- unique(group_var) if(length(group_levels) != 2) { stop('wilcox.test is only applied to two group comparisons.') } ref <- group_levels[group_levels != reference_group] group_var <- relevel(factor(group_var), ref = ref) # perform test tst <- map(norm, function(x) { wilcox.test(x ~ group_var, conf.int = TRUE, ...) }) # make a tidy data.frame or return htest object if(tidy) { res <- bind_rows(map(tst, function(x) { data_frame( estimate = unname(x$estimate), p_value = x$p.value, lower = x$conf.int[1], upper = x$conf.int[2] ) }), .id = 'gene') } else { res <- tst } # return return(res) } #' Linear regression qPCR data #' #' @inheritParams pcr_ddct #' @param model_matrix A model matrix for advanced experimental design. for #' constructing such a matrix with different variables check #' \code{\link[stats]{model.matrix}} #' @param mode A character string for the normalization mode. Possible values #' are "subtract" (default) or "divide". #' @param tidy A \code{logical} whether to return a \code{list} of #' \code{\link[stats]{lm}} or a tidy \code{data.frame}. Default TRUE. #' @param ... Other arguments to \code{\link[stats]{lm}} #' #' @return A data.frame of 6 columns #' \itemize{ #' \item term The term being tested #' \item gene The column names of df. reference_gene is dropped #' \item estimate The estimate for each term #' \item p_value The p-value for each term #' \item lower The low 95\% confidence interval #' \item upper The high 95\% confidence interval #' } #' When \code{tidy} is FALSE, returns a \code{list} of \code{\link[stats]{lm}} #' objects. #' #' @examples #' # locate and read data #' fl <- system.file('extdata', 'ct4.csv', package = 'pcr') #' ct4 <- readr::read_csv(fl) #' #' # make group variable #' group <- rep(c('control', 'treatment'), each = 12) #' #' # test #' pcr_lm(ct4, #' group_var = group, #' reference_gene = 'ref', #' reference_group = 'control') #' #' # testing using lm method #' pcr_test(ct4, #' group_var = group, #' reference_gene = 'ref', #' reference_group = 'control', #' test = 'lm') #' #' @importFrom purrr map #' @importFrom stats lm confint relevel #' @importFrom dplyr data_frame bind_rows #' #' @export pcr_lm <- function(df, group_var, reference_gene, reference_group, model_matrix = NULL, mode = 'subtract', tidy = TRUE, ...) { # calculate the delta_ct values norm <- .pcr_normalize(df, reference_gene = reference_gene, mode = mode) # adjust group_var for formuls if(is.null(model_matrix)) { group_var <- relevel(factor(group_var), ref = reference_group) } # apply linear models tst <- map(norm, function(x) { if(is.null(model_matrix)) { lm(x ~ group_var, ...) } else { lm(x ~ model_matrix + 0, ...) } }) # make a tidy data.frame or return wilcox object if(tidy) { res <- bind_rows(map(tst, function(x) { mod <- x conf_int <- confint(mod) data_frame( term = names(mod$coefficients)[-1], estimate = unname(mod$coefficients)[-1], p_value = summary(mod)$coefficients[-1, 4], lower = conf_int[-1, 1], upper = conf_int[-1, 2] ) }), .id = 'gene') } else { res <- tst } # return return(res) }

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/tests/testthat/test-expect_s3_class_linter.R

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[ "MIT" ]

permissive

cordis-dev/lintr

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cb694d5e4da927f56c88fa5d8972594a907be59a

refs/heads/main

2023-08-05T08:50:42.679421

2023-07-25T13:21:29

225,583,354

0

NOASSERTION

2019-12-03T09:41:30

null

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R

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r

test-expect_s3_class_linter.R

test_that("expect_s3_class_linter skips allowed usages", { linter <- expect_s3_class_linter() # expect_s3_class doesn't have an inverted version expect_lint("expect_true(!inherits(x, 'class'))", NULL, linter) # NB: also applies to tinytest, but it's sufficient to test testthat expect_lint("testthat::expect_true(!inherits(x, 'class'))", NULL, linter) # other is.<x> calls are not suitable for expect_s3_class in particular expect_lint("expect_true(is.na(x))", NULL, linter) # case where expect_s3_class() *could* be used but we don't enforce expect_lint("expect_true(is.data.table(x))", NULL, linter) # expect_s3_class() doesn't have info= or label= arguments expect_lint("expect_equal(class(x), k, info = 'x should have class k')", NULL, linter) expect_lint("expect_equal(class(x), k, label = 'x class')", NULL, linter) expect_lint("expect_equal(class(x), k, expected.label = 'target class')", NULL, linter) expect_lint("expect_true(is.data.frame(x), info = 'x should be a data.frame')", NULL, linter) }) test_that("expect_s3_class_linter blocks simple disallowed usages", { expect_lint( "expect_equal(class(x), 'data.frame')", rex::rex("expect_s3_class(x, k) is better than expect_equal(class(x), k)"), expect_s3_class_linter() ) # works when testing against a sequence of classes too expect_lint( "expect_equal(class(x), c('data.table', 'data.frame'))", rex::rex("expect_s3_class(x, k) is better than expect_equal(class(x), k)"), expect_s3_class_linter() ) # expect_identical is treated the same as expect_equal expect_lint( "testthat::expect_identical(class(x), 'lm')", rex::rex("expect_s3_class(x, k) is better than expect_identical(class(x), k)"), expect_s3_class_linter() ) # yoda test with string literal in first arg also caught expect_lint( "expect_equal('data.frame', class(x))", rex::rex("expect_s3_class(x, k) is better than expect_equal(class(x), k)"), expect_s3_class_linter() ) # different equivalent usages expect_lint( "expect_true(is.table(foo(x)))", rex::rex("expect_s3_class(x, k) is better than expect_true(is.<k>(x))"), expect_s3_class_linter() ) expect_lint( "expect_true(inherits(x, 'table'))", rex::rex("expect_s3_class(x, k) is better than expect_true(is.<k>(x))"), expect_s3_class_linter() ) # TODO(michaelchirico): consider more carefully which sorts of class(x) %in% . and # . %in% class(x) calls should be linted #> expect_lint( #> "expect_true('lm' %in% class(x))", #> "expect_s3_class\$x, k\$ is better than expect_equal\$class\\(x\$, k", #> expect_s3_class_linter #> ) }) local({ # test for lint errors appropriately raised for all is.<class> calls is_classes <- c( "data.frame", "factor", "numeric_version", "ordered", "package_version", "qr", "table", "relistable", "raster", "tclObj", "tkwin", "grob", "unit", "mts", "stepfun", "ts", "tskernel" ) patrick::with_parameters_test_that( "expect_true(is.<base class>) is caught", expect_lint( sprintf("expect_true(is.%s(x))", is_class), rex::rex("expect_s3_class(x, k) is better than expect_true(is.<k>(x))"), expect_s3_class_linter() ), .test_name = is_classes, is_class = is_classes ) })

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/codeml_files/newick_trees_processed/8734_0/rinput.R

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[]

no_license

DaniBoo/cyanobacteria_project

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be08ff723284b0c38f9c758d3e250c664bbfbf3b

refs/heads/master

2021-01-25T05:28:00.686474

2013-03-23T15:09:39

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r

rinput.R

library(ape) testtree <- read.tree("8734_0.txt") unrooted_tr <- unroot(testtree) write.tree(unrooted_tr, file="8734_0_unrooted.txt")

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/dataproc/workerMetrics.R

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[]

no_license

laroyo/watsonc

9489605d94c1336a82350a2dc4494e54374dc624

a55b62945ed75b85e81ec5be453ccb8a3edd6a25

refs/heads/master

2016-09-05T23:13:48.119320

2015-06-17T10:21:34

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workerMetrics.R

#!/usr/bin/Rscript ## Read file 90-sents-all-batches-GS-sentsv3.csv and applies the filters. ## The filter output is the same as 90-sents-all-batches-CS-sentsv3.csv (Dropbox/data/CF-Results-processed/) source('/var/www/html/wcs/dataproc/envars.R') library(XLConnect) source(paste(libpath,'/db.R',sep=''),chdir=TRUE) source(paste(libpath,'/measures.R',sep=''),chdir=TRUE) source(paste(libpath,'/filters.R',sep=''),chdir=TRUE) source(paste(libpath,'/simplify.R',sep=''),chdir=TRUE) source(paste(libpath,'/fileStorage.R',sep=''),chdir=TRUE) #For calculating the cosine. library(lsa) args <- commandArgs(trailingOnly = TRUE) if(length(args) > 0){ job.id <- args[1] } else { if(!exists('job.id')){ stop('Error: you should provide a Job id (parameter)') } } #FIXME: this be obtained when storing the file on the file storage. file_id <- -1 raw.data <- getJob(job.id) if(job.id == 196344){ raw.data <- raw.data[raw.data$relation != '',] } worker.ids <- sort(unique(raw.data$worker_id)) without.singletons <- TRUE if(without.singletons) { numSent <- numSentences(raw.data) singletons <- belowFactor(numSent,'numSent',3) worker.ids <- setdiff(worker.ids,singletons) raw.data <- raw.data[!(raw.data$worker_id %in% singletons),] } if(dim(raw.data)[1] == 0){ cat('JOB_NOT_FOUND') } else { sentenceTable <- pivot(raw.data,'unit_id','relation') sentenceDf <- getDf(sentenceTable) #Calculate the measures to apply the filters. filters <- list('SQRT','NormSQRT','NormR', 'NormRAll') #Calculate the measures to apply the filters.filters <- list('SQRT','NormSQRT') mdf <- calc_measures(sentenceDf,filters) discarded <- list() filtered <- list() for (f in filters){ #Apply the filters: each one returns the discarded rows (those below the threshold) discarded[[f]] <- belowDiff(mdf,f) #The filtered *in* filtered[[f]] <- setdiff(rownames(sentenceDf),discarded[[f]]) saveFilteredSentences(job.id, file_id, f, discarded[[f]]) } #After applying the filters, add the "NULL" filter. filters <- append('NULL', filters) filtered[['NULL']] <- rownames(sentenceDf) discarded[['NULL']] <- NULL worker.ids <- sort(unique(raw.data$worker_id)) out <- NULL spamCandidates <- list() for (f in filters){ print(paste('computing metrics for filter ',f)) filt <- raw.data[raw.data$unit_id %in% filtered[[f]],] filtWorkers <- sort(unique(filt$worker_id)) numSent <- numSentences(filt) numAnnot <- numAnnotations(filt) annotSentence <- numAnnot / numSent colnames(annotSentence) <- 'annotSentence' #sentMat <- list() agrValues <- agreement(filt) cosValues <- cosMeasure(filt) #sentRelScoreValues <- sentRelScoreMeasure(filt) saveWorkerMetrics(cbind(agrValues, cosValues,annotSentence,numSent), job.id, f,without.singletons) #df <- data.frame(row.names=filtWorkers,numSents=numSent, cos=cosValues, agr=agrValues, annotSentence=(numAnnot/numSent)) df <- cbind(numSent,cosValues, agrValues,annotSentence) #Add empty values for filtered out workers missingworkers <- setdiff(worker.ids,filtWorkers) emptyCol <- rep(0,length(missingworkers)) filtrows <- data.frame(row.names=missingworkers,numSent=emptyCol,cos=emptyCol,agr=emptyCol,annotSentence=emptyCol) df <- rbind(df, filtrows) df <- df[order(as.numeric(row.names(df))),] #Empty dataframe spamFilters <- data.frame(row.names=worker.ids,cos=rep(0,length(worker.ids)),annotSentence=rep(0,length(worker.ids)),agr=rep(0,length(worker.ids))) candidateRows <- belowDiff(df,'cos') if(length(candidateRows) > 0 & dim(spamFilters[rownames(spamFilters) %in% candidateRows,])[1]>0){ spamFilters[rownames(spamFilters) %in% candidateRows,]$cos = 1 } spammers <- c(14067668,9705524,12974606,14119448,9844590,8071333,13997142,8885952,7478095,9767020,13617382,5254360,8947442) candidateRows <- overDiff(df,'annotSentence') if(length(candidateRows) > 0 & dim(spamFilters[rownames(spamFilters) %in% candidateRows,])[1]>0){ #if(length(candidateRows) > 0){ spamFilters[rownames(spamFilters) %in% candidateRows,]$annotSentence = 1 } candidateRows <- belowDiff(df,'agr') if(length(candidateRows) > 0 & dim(spamFilters[rownames(spamFilters) %in% candidateRows,])[1]>0){ #if(length(candidateRows) > 0){ spamFilters[rownames(spamFilters) %in% candidateRows,]$agr = 1 } spamCandidates[[f]] <- spamFilters if(is.null(out)){ out <- df } else { out <- cbind(out, df) } } spamFilterOutput <- data.frame(row.names=worker.ids, filter1=rowSums(spamCandidates[['NULL']]), filter2=rowSums(spamCandidates[['SQRT']]), filter3=rowSums(spamCandidates[['NormSQRT']]), filter4=rowSums(spamCandidates[['NormR']]), filter5=rowSums(spamCandidates[['NormRAll']]) ) #Combine spamFilterOutput. sf <- as.data.frame(rowSums(spamFilterOutput > 1) > 1) colnames(sf) = 'label' spamLabels <- rownames(sf[sf$label==TRUE,,drop=FALSE]) fname <- getFileName(job.id,fileTypes[['workerMetrics']]) path <- getFilePath(job.id, folderTypes[['analysisFiles']], FALSE) wb.new <- loadWorkbook(paste(path,fname,sep='/'), create = TRUE) sentRelDf <- sentRelScoreMeasure(raw.data) sClarity <- sentenceClarity(sentRelDf) rClarity <- relationClarity(sentRelDf) workerSentCos <- workerSentenceCosTable(raw.data) workerSentScore <- workerSentenceScoreTable(raw.data, workerSentCos, sClarity) #workerRelScore <- workerRelationScore(raw.data, rClarity, workerSentCos) ## createSheet(wb.new, name = "pivot-worker") ## writeOutputHeaders(wb.new,"pivot-worker") ## writeWorksheet(wb.new,data=cbind(out,spamFilterOutput[rownames(out),],spam=sf[rownames(out),]),sheet=1,startRow=2,startCol=1,header=TRUE,rownames='Worker ID') query <- sprintf("select worker_id, relation,explanation,selected_words,sentence from cflower_results where job_id = %s", job.id) res <- dbGetQuery(con,query) res$selected_words <- apply(res[,'selected_words',drop=FALSE],1,FUN=correctMisspells) res$explanation <- apply(res[,'explanation',drop=FALSE],1,FUN=correctMisspells) oth.non <- res[intersect(grep('OTHER|NONE',res$relation),grep('\n',res$relation)),] filtWorkers <- list() if(dim(oth.non)[1] > 0){ filtWorkers[['none_other']] <- noneOther(oth.non) } filtWorkers[['rep_response']] <- repeatedResponse(res) filValWords <- validWords(res) filtWorkers[['valid_words']] <- sort(unique(filValWords$worker_id)) filtWorkers[['rep_text']] <- repeatedText(job.id,'both') beh.filters <- c('none_other', 'rep_response','valid_words', 'rep_text') bspammers <- c() for (f in beh.filters){ for (f2 in beh.filters){ if(f != f2) bspammers <- union(bspammers,intersect(filtWorkers[[f]],filtWorkers[[f2]])) } } saveFilteredWorkers(job.id,unique(bspammers),'beh_filters') ## for (filter in names(filtWorkers)){ ## saveFilteredWorkers(job.id, filtWorkers[[filter]], filter) ## } saveFilteredWorkers(job.id, spamLabels, 'disag_filters') numFilteredSentences <- length(unlist(discarded)) numWorkers <- length(unique(raw.data$worker_id)) numFilteredWorkers <- length(union(spamLabels, unique(unlist(filtWorkers)))) query <- sprintf("update history_table set no_workers = %s, no_filtered_workers = %s where job_id = %s", numWorkers, numFilteredWorkers, job.id) rs <- dbSendQuery(con, query) createSheet(wb.new, name = "singleton-workers-removed") writeOutputHeaders(wb.new,"singleton-workers-removed") writeWorksheet(wb.new,data=out[rownames(out) %in% setdiff(rownames(out),singletons),],sheet="singleton-workers-removed",startRow=2,startCol=1,header=TRUE,rownames='Worker ID') ## createSheet(wb.new, name="workerRelationScore") ## wrs <- workerRelScore ## wrs[is.na(workerRelScore)] <- 0 ## wrs$worker_id <- rownames(wrs) ## writeWorksheet(wb.new,data=wrs[,all],sheet="workerRelationScore",startRow=1,startCol=1,header=TRUE,rownames='Worker ID') createSheet(wb.new, name = "filtered-out-sentences") writeFilteredOutHeaders(wb.new,"filtered-out-sentences") currentCol <- 1 for (f in filters){ if(f != 'NULL'){ writeWorksheet(wb.new,data=discarded[[f]],sheet='filtered-out-sentences',startRow=2,startCol=currentCol,header=FALSE) currentCol <- currentCol + 2 #write.csv(discarded[[f]], paste(outputdirectory,paste(job_id,'filtered-out-sentences',f,'.csv',sep="_"),sep=""),row.names=FALSE) } } createSheet(wb.new, name = "spammer-labels") writeWorksheet(wb.new,data=spamLabels,sheet='spammer-labels',startRow=1,startCol=1,header=FALSE) createSheet(wb.new, name = "beh-filters") writeBehFiltersHeaders(wb.new, 'beh-filters') writeWorksheet(wb.new,filtWorkers[['none_other']],sheet='beh-filters',startRow=2,startCol=1,header=FALSE) writeWorksheet(wb.new,filtWorkers[['rep_response']],sheet='beh-filters',startRow=2,startCol=3,header=FALSE) writeWorksheet(wb.new,filtWorkers[['valid_words']],sheet='beh-filters',startRow=2,startCol=5,header=FALSE) writeWorksheet(wb.new,filtWorkers[['rep_text']],sheet='beh-filters',startRow=2,startCol=7,header=FALSE) saveWorkbook(wb.new) #FIXME: get the adecuate value for the creator creator = 'script' saveFileMetadata(fname,path,mimeTypes[['excel']],-1,creator) dbDisconnect(con) cat('OK') }

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c5904577c015ffd7254fef31eae73484aa0fc6a7

/fitAndCompareModels.R

92efad90591a072c5aa8dcb8729cc1260f681ad6

[]

no_license

RetoSchmucki/SURPASS_WP1

a11e56a6eaa79d8b5e78f9bf9db42715e6514683

e103caea6b83dbb009511954acd70b96db19653c

refs/heads/main

2023-06-11T03:03:08.761942

2021-07-01T10:55:35

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fitAndCompareModels.R

#### fit models to estimate temporal trends in species' distributions #### library(occAssess) library(raster) library(reshape2) library(plyr) library(dplyr) library(ggplot2) library(gridExtra) # Load sparta library(sparta) ## setup model grid shp <- raster::shapefile("C:/Users/Rob.Lenovo-PC/Documents/surpass/Data/South America country boundaries/South America country boundaries/data/commondata/data0/southamerica_adm0.shp") shp <- shp[shp$COUNTRY == "CHILE", ] shp <- spTransform(shp, crs("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")) grid <- raster("C:/Users/Rob.Lenovo-PC/Documents/surpass/Data/maskLayers/mask_CHL.asc") grid <- crop(grid, shp) grid <- aggregate(grid, fact = 6) ## load species data dat <- read.csv("C:/Users/Rob.Lenovo-PC/Documents/surpass/Data/GBIF/07.04.21/preAndPostDigBeesChile.csv") dat <- dat[-which(is.na(dat$species)), ] pre <- dat[dat$identifier == "pre-digitization", ] post <- dat[dat$identifier == "post-digitization", ] ## format species data for use with sparta models formatDat <- function(data, x) { cell <- extract(grid, data.frame(x = data$x[x], y = data$y[x]), cellnumbers = TRUE) cell <- cell[1] data.frame(species = data$species[x], cell = cell, year = data$year[x]) } fDatPre <- lapply(1:nrow(pre), formatDat, data = pre) fDatPre <- do.call("rbind", fDatPre) fDatPost <- lapply(1:nrow(post), formatDat, data = post) fDatPost <- do.call("rbind", fDatPost) ## drop data from Chilean island outside of domain fDatPost <- fDatPost[-which(is.na(fDatPost$cell)), ] periods <- list(1950:1959, 1960:1969, 1970:1979, 1980:1989, 1990:1999,2000:2010, 2011:2019) fDatPre$Period <- NA fDatPost$Period <- NA for (i in 1: length(periods)) { fDatPre$Period <- ifelse(fDatPre$year %in% periods[[i]], i, fDatPre$Period) fDatPost$Period <- ifelse(fDatPost$year %in% periods[[i]], i, fDatPost$Period) } #fDatPre <- fDatPre[-which(is.na(fDatPre$cell)), ] #fDatPost <- fDatPost[-which(is.na(fDatPost$cell)), ] ## fit models # first the reporting rate model with list length as a covariate and a random site intercept rrPre <- reportingRateModel(taxa = fDatPre$species, site = fDatPre$cell, time_period = as.numeric(fDatPre$Period), list_length = TRUE, site_effect = TRUE) rrPost <- reportingRateModel(taxa = fDatPost$species, site = fDatPost$cell, time_period = as.numeric(fDatPost$Period), list_length = TRUE, site_effect = TRUE) ## check for models that didn't converge length(rrPre[!is.na(rrPre$error_message), ]) length(rrPost[!is.na(rrPost$error_message), ]) ## then a simpler model without the random site intercept rr2Pre <- reportingRateModel(taxa = fDatPre$species, site = fDatPre$cell, time_period = as.numeric(fDatPre$Period), list_length = TRUE, site_effect = FALSE) rr2Post <- reportingRateModel(taxa = fDatPost$species, site = fDatPost$cell, time_period = as.numeric(fDatPost$Period), list_length = TRUE, site_effect = FALSE) ## compare models rrMods <- merge(rrPre, rrPost, by = "species_name") rr2Mods <- merge(rr2Pre, rr2Post, by = "species_name") ## check species converged in BOTH models nrow(rrMods[is.na(rrMods$error_message.x) & is.na(rrMods$error_message.y), ]) nrow(rr2Mods) ## plot predictions from post-digitization data against those from pre digitization data plot(rrMods$year.estimate.y ~ rrMods$year.estimate.x) plot(rr2Mods$year.estimate.y ~ rr2Mods$year.estimate.x) cor.test(rrMods$year.estimate.y, rrMods$year.estimate.x) cor.test(rr2Mods$year.estimate.y, rr2Mods$year.estimate.x) ## now format the data for the Telfer model fDatTelfPre <- fDatPre[fDatPre$Period %in% c(1,2,3, 5, 6, 7), ] # p1 = decades 1, 2 and 3; and p2 = decades 5, 6 and 7 fDatTelfPre$Period <- ifelse(fDatTelfPre$Period %in% c(1,2, 3), 1, 2) fDatTelfPost <- fDatPost[fDatPost$Period %in% c(1,2,3,5,6,7), ] fDatTelfPost$Period <- ifelse(fDatTelfPost$Period %in% c(1,2,3), 1, 2) ## fit Telfer model telferPre <- sparta::telfer(taxa = fDatTelfPre$species, site = fDatTelfPre$cell, time_period = as.numeric(fDatTelfPre$Period), minSite = 2) telferPost <- sparta::telfer(taxa = fDatTelfPost$species, site = fDatTelfPost$cell, time_period = as.numeric(fDatTelfPost$Period), minSite = 2) colnames(telferPre)[1] <- "species_name" colnames(telferPost)[1] <- "species_name" ## compare Telfer models telferMods <- merge(telferPre, telferPost, by = "species_name") ## establish number of species which could be fitted using both pre and post-digitization data nrow(telferMods[!is.na(telferMods$Telfer_1_2.x) & !is.na(telferMods$Telfer_1_2.y), ]) ## plot model predictions from post-digitization data on predictions from pre-digitization data pTelfer <- ggplot(data = telferMods, aes(x = Telfer_1_2.x, y = Telfer_1_2.y)) + geom_point() + theme_linedraw() + xlab("pre digitization index") + ylab("post digitization index") + geom_abline(slope = 1, intercept = 0) + ggtitle("A) Telfer") pRR <- ggplot(data = rrMods, aes(x = year.estimate.x, y = year.estimate.y)) + geom_point() + theme_linedraw() + xlab("pre digitization index") + ylab("post digitization index") + geom_abline(slope = 1, intercept = 0) + ggtitle("C) RR + site") pRR2 <- ggplot(data = rr2Mods, aes(x = year.estimate.x, y = year.estimate.y)) + geom_point() + theme_linedraw() + xlab("pre digitization index") + ylab("post digitization index") + geom_abline(slope = 1, intercept = 0) + ggtitle("B) RR") png("preVsPostMods.png", width = 3, height = 9, units = "in", res = 500) grid.arrange(pTelfer, pRR2, pRR, ncol = 1) dev.off() cor.test(telferMods$Telfer_1_2.x, telferMods$Telfer_1_2.y) plot(telferMods$Telfer_1_2.y ~ rrMods$year.estimate.y) cor.test(mods$year.estimate, mods$Telfer_1_2, method = "spearman") plot(log(mods$year.estimate) ~ log(mods$Telfer_1_2)) meanRR <- median(mods$year.estimate, na.rm = T) mods$agree <- ifelse(mods$year.estimate > meanRR & mods$Telfer_1_2 > 0 | mods$year.estimate < meanRR & mods$Telfer_1_2 < 0, "agree", "disagree") head(mods) png("cor.png", width = 5, height = 5, units = "in", res = 500) ggplot(data = mods, aes(x = Telfer_1_2, y = year.estimate, group = agree, colour = agree)) + geom_point() + theme_linedraw() + ylim(c(-3, 3)) + xlab("Telfer index") + ylab("RR period effect") + ggtitle("Spearman's rho = 0.59") + theme(legend.position = "none") dev.off()

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/R/f7List.R

4cc4cf33559ffc26ec8bba3b9af7a2d393c230b4

[]

no_license

RinteRface/shinyMobile

a8109cd39c85e171db893d1b3f72d5f1a04f2c62

86d36f43acf701b6aac42d716adc1fae4f8370c6

refs/heads/master

2023-07-25T16:28:41.026349

2022-11-25T17:04:29

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2023-03-26T05:58:53

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r

f7List.R

#' Create a framework 7 contact list #' #' @param ... Slot for \link{f7ListGroup} or \link{f7ListItem}. #' @param mode List mode. NULL or "media" or "contacts". #' @param inset Whether to display a card border. FALSE by default. #' @export #' #' @examples #' if (interactive()) { #' library(shiny) #' library(shinyMobile) #' #' shinyApp( #' ui = f7Page( #' title = "My app", #' f7SingleLayout( #' navbar = f7Navbar(title = "f7List"), #' #' # simple list #' f7List( #' lapply(1:3, function(j) f7ListItem(letters[j])) #' ), #' #' # list with complex items #' f7List( #' lapply(1:3, function(j) { #' f7ListItem( #' letters[j], #' media = f7Icon("alarm_fill"), #' right = "Right Text", #' header = "Header", #' footer = "Footer" #' ) #' }) #' ), #' #' # list with complex items #' f7List( #' mode = "media", #' lapply(1:3, function(j) { #' f7ListItem( #' title = letters[j], #' subtitle = "subtitle", #' "Lorem ipsum dolor sit amet, consectetur adipiscing elit. #' Nulla sagittis tellus ut turpis condimentum, ut dignissim #' lacus tincidunt. Cras dolor metus, ultrices condimentum sodales #' sit amet, pharetra sodales eros. Phasellus vel felis tellus. #' Mauris rutrum ligula nec dapibus feugiat. In vel dui laoreet, #' commodo augue id, pulvinar lacus.", #' media = tags$img( #' src = paste0( #' "https://cdn.framework7.io/placeholder/people-160x160-", j, ".jpg" #' ) #' ), #' right = "Right Text" #' ) #' }) #' ), #' #' # list with links #' f7List( #' lapply(1:3, function(j) { #' f7ListItem(url = "https://google.com", letters[j]) #' }) #' ), #' #' # grouped lists #' f7List( #' mode = "contacts", #' lapply(1:3, function(i) { #' f7ListGroup( #' title = LETTERS[i], #' lapply(1:3, function(j) f7ListItem(letters[j])) #' ) #' }) #' ) #' ) #' ), #' server = function(input, output) {} #' ) #' } f7List <- function(..., mode = NULL, inset = FALSE) { listCl <- "list chevron-center" if (!is.null(mode)) listCl <- paste0(listCl, " ", mode, "-list") if (inset) listCl <- paste0(listCl, " inset") shiny::tags$div( class = listCl, if (is.null(mode)) { shiny::tags$ul(...) } else if (mode == "media") { shiny::tags$ul(...) } else { shiny::tagList(...) } ) } #' Create a Framework 7 contact item #' #' @param ... Item text. #' @param title Item title. #' @param subtitle Item subtitle. #' @param header Item header. Do not use when \link{f7List} mode is not NULL. #' @param footer Item footer. Do not use when \link{f7List} mode is not NULL. #' @param href Item external link. #' @param media Expect \link{f7Icon} or \code{img}. #' @param right Right content if any. #' @export f7ListItem <- function(..., title = NULL, subtitle = NULL, header = NULL, footer = NULL, href = NULL, media = NULL, right = NULL) { # avoid to have crazy large images if (!is.null(media)) { if (!is.null(media$name)) { if (media$name == "img") media$attribs$width <- "50" } } itemContent <- shiny::tagList( # left media if (!is.null(media)) { shiny::tags$div( class = "item-media", media ) }, # center content shiny::tags$div( class = "item-inner", if (is.null(title)) { shiny::tagList( shiny::tags$div( class = "item-title", if (!is.null(header)) { shiny::tags$div( class = "item-header", header ) }, ..., if (!is.null(footer)) { shiny::tags$div( class = "item-footer", footer ) } ), # right content if (!is.null(right)) { shiny::tags$div( class = "item-after", right ) } ) } else { shiny::tagList( shiny::tags$div( class = "item-title-row", shiny::tags$div( class = "item-title", if (!is.null(header)) { shiny::tags$div( class = "item-header", header ) }, title, if (!is.null(footer)) { shiny::tags$div( class = "item-footer", footer ) } ), # right content if (!is.null(right)) { shiny::tags$div( class = "item-after", right ) } ), # subtitle if (!is.null(subtitle)) { shiny::tags$div( class = "item-subtitle", subtitle ) }, # text shiny::tags$div( class = "item-text", ... ) ) } ) ) itemContentWrapper <- if (is.null(href)) { shiny::tags$div( class = "item-content", itemContent ) } else { shiny::tags$a( class = "item-link item-content external", href = href, target = "_blank", itemContent ) } shiny::tags$li(itemContentWrapper) } #' Create a framework 7 group of contacts #' #' @param ... slot for \link{f7ListItem}. #' @param title Group title. #' @export f7ListGroup <- function(..., title) { shiny::tags$div( class = "list-group", shiny::tags$ul( shiny::tags$li(class = "list-group-title", title), ... ) ) } #' Create a Framework 7 list index #' #' List index must be attached to an existing list view. #' #' @param id Unique id. #' @param target Related list element. CSS selector like .class, #id, ... #' @param ... Other options (see \url{https://v5.framework7.io/docs/list-index#list-index-parameters}). #' @param session Shiny session object. #' @export #' #' @note For some reason, unable to get more than 1 list index working. See #' example below. The second list does not work. #' #' @examples #' if (interactive()) { #' library(shiny) #' library(shinyMobile) #' shinyApp( #' ui = f7Page( #' title = "List Index", #' f7TabLayout( #' navbar = f7Navbar( #' title = "f7ListIndex", #' hairline = FALSE, #' shadow = TRUE #' ), #' f7Tabs( #' f7Tab( #' tabName = "List1", #' f7List( #' mode = "contacts", #' lapply(1:26, function(i) { #' f7ListGroup( #' title = LETTERS[i], #' lapply(1:26, function(j) f7ListItem(letters[j])) #' ) #' }) #' ) #' ), #' f7Tab( #' tabName = "List2", #' f7List( #' mode = "contacts", #' lapply(1:26, function(i) { #' f7ListGroup( #' title = LETTERS[i], #' lapply(1:26, function(j) f7ListItem(letters[j])) #' ) #' }) #' ) #' ) #' ) #' ) #' ), #' server = function(input, output, session) { #' observeEvent(TRUE, { #' f7ListIndex(id = "list-index-1", target = ".list") #' }, once = TRUE) #' } #' ) #' } f7ListIndex <- function(id, target, ..., session = shiny::getDefaultReactiveDomain()) { message <- list(el = id, listEl = target, ...) sendCustomMessage("listIndex", message, session) } #' Framework7 virtual list #' #' \code{f7VirtualList} is a high performance list container. #' Use if you have too many components in \link{f7List}. #' #' @param id Virtual list unique id. #' @param items List items. Slot for \link{f7VirtualListItem}. #' @param rowsBefore Amount of rows (items) to be rendered before current #' screen scroll position. By default it is equal to double amount of #' rows (items) that fit to screen. #' @param rowsAfter Amount of rows (items) to be rendered after current #' screen scroll position. By default it is equal to the amount of rows #' (items) that fit to screen. #' @param cache Disable or enable DOM cache for already rendered list items. #' In this case each item will be rendered only once and all further #' manipulations will be with DOM element. It is useful if your list #' items have some user interaction elements (like form elements or swipe outs) #' or could be modified. #' #' @export #' @rdname virtuallist #' @examples #' if (interactive()) { #' library(shiny) #' library(shinyMobile) #' shinyApp( #' ui = f7Page( #' title = "Virtual List", #' f7SingleLayout( #' navbar = f7Navbar( #' title = "Virtual Lists", #' hairline = FALSE, #' shadow = TRUE #' ), #' # main content #' f7VirtualList( #' id = "vlist", #' rowsBefore = 2, #' rowsAfter = 2, #' items = lapply(1:2000, function(i) { #' f7VirtualListItem( #' title = paste("Title", i), #' subtitle = paste("Subtitle", i), #' header = paste("Header", i), #' footer = paste("Footer", i), #' right = paste("Right", i), #' content = i, #' media = img(src = "https://cdn.framework7.io/placeholder/fashion-88x88-1.jpg") #' ) #' }) #' ) #' ) #' ), #' server = function(input, output) { #' #' } #' ) #' #' # below example will not load with classic f7List #' #shinyApp( #' # ui = f7Page( #' # title = "My app", #' # f7SingleLayout( #' # navbar = f7Navbar( #' # title = "Virtual Lists", #' # hairline = FALSE, #' # shadow = TRUE #' # ), #' # # main content #' # f7List( #' # lapply(1:20000, function(i) { #' # f7ListItem( #' # title = paste("Title", i), #' # subtitle = paste("Subtitle", i), #' # header = paste("Header", i), #' # footer = paste("Footer", i), #' # right = paste("Right", i), #' # content = i #' # ) #' # }) #' # ) #' # ) #' # ), #' # server = function(input, output) { #' # #' # } #' #) #' } f7VirtualList <- function(id, items, rowsBefore = NULL, rowsAfter = NULL, cache = TRUE) { config <- dropNulls( list( items = items, rowsBefore = rowsBefore, rowsAfter = rowsAfter, cache = cache ) ) shiny::tags$div( id = id, shiny::tags$script( type = "application/json", `data-for` = id, jsonlite::toJSON( x = config, auto_unbox = TRUE, json_verbatim = TRUE ) ), class = "list virtual-list media-list searchbar-found" ) } #' Framework7 virtual list item #' #' \code{f7VirtualListItem} is an item component for \link{f7VirtualList}. #' #' @inheritParams f7ListItem #' @rdname virtuallist #' @export f7VirtualListItem <- function(..., title = NULL, subtitle = NULL, header = NULL, footer = NULL, href = NULL, media = NULL, right = NULL) { dropNulls( list( content = ..., title = title, subtitle = subtitle, header = header, footer = footer, url = href, media = as.character(media), # avoid issue on JS side right = right ) ) } #' Update an \link{f7VirtualList} on the server side #' #' This function wraps all methods from \url{https://framework7.io/docs/virtual-list.html} #' #' @param id \link{f7VirtualList} to update. #' @param action Action to perform. See \url{https://framework7.io/docs/virtual-list.html}. #' @param item If action is one of appendItem, prependItem, replaceItem, insertItemBefore. #' @param items If action is one of appendItems, prependItems, replaceAllItems. #' @param index If action is one of replaceItem, insertItemBefore, deleteItem. #' @param indexes If action if one of filterItems, deleteItems. #' @param oldIndex If action is moveItem. #' @param newIndex If action is moveItem. #' @param session Shiny session. #' #' @export #' #' @examples #' if (interactive()) { #' library(shiny) #' library(shinyMobile) #' shinyApp( #' ui = f7Page( #' title = "Update virtual list", #' f7SingleLayout( #' navbar = f7Navbar( #' title = "Virtual Lists", #' hairline = FALSE, #' shadow = TRUE #' ), #' # main content #' f7Segment( #' container = "segment", #' #' f7Button(inputId = "appendItem", "Append Item"), #' f7Button(inputId = "prependItems", "Prepend Items"), #' f7Button(inputId = "insertBefore", "Insert before"), #' f7Button(inputId = "replaceItem", "Replace Item") #' ), #' f7Segment( #' container = "segment", #' f7Button(inputId = "deleteAllItems", "Remove All"), #' f7Button(inputId = "moveItem", "Move Item"), #' f7Button(inputId = "filterItems", "Filter Items") #' ), #' f7Flex( #' uiOutput("itemIndexUI"), #' uiOutput("itemNewIndexUI"), #' uiOutput("itemsFilterUI") #' ), #' f7VirtualList( #' id = "vlist", #' items = lapply(1:5, function(i) { #' f7VirtualListItem( #' title = paste("Title", i), #' subtitle = paste("Subtitle", i), #' header = paste("Header", i), #' footer = paste("Footer", i), #' right = paste("Right", i), #' content = i, #' media = img(src = "https://cdn.framework7.io/placeholder/fashion-88x88-3.jpg") #' ) #' }) #' ) #' ) #' ), #' server = function(input, output, session) { #' #' output$itemIndexUI <- renderUI({ #' req(input$vlist$length > 2) #' f7Stepper( #' inputId = "itemIndex", #' label = "Index", #' min = 1, #' value = 2, #' max = input$vlist$length #' ) #' }) #' #' output$itemNewIndexUI <- renderUI({ #' req(input$vlist$length > 2) #' f7Stepper( #' inputId = "itemNewIndex", #' label = "New Index", #' min = 1, #' value = 1, #' max = input$vlist$length #' ) #' }) #' #' output$itemsFilterUI <- renderUI({ #' input$appendItem #' input$prependItems #' input$insertBefore #' input$replaceItem #' input$deleteAllItems #' input$moveItem #' isolate({ #' req(input$vlist$length > 2) #' f7Slider( #' inputId = "itemsFilter", #' label = "Items to Filter", #' min = 1, #' max = input$vlist$length, #' value = c(1, input$vlist$length) #' ) #' }) #' }) #' #' observe(print(input$vlist)) #' #' observeEvent(input$appendItem, { #' updateF7VirtualList( #' id = "vlist", #' action = "appendItem", #' item = f7VirtualListItem( #' title = "New Item Title", #' right = "New Item Right", #' content = "New Item Content", #' media = img(src = "https://cdn.framework7.io/placeholder/fashion-88x88-1.jpg") #' ) #' ) #' }) #' #' observeEvent(input$prependItems, { #' updateF7VirtualList( #' id = "vlist", #' action = "prependItems", #' items = lapply(1:5, function(i) { #' f7VirtualListItem( #' title = paste("Title", i), #' right = paste("Right", i), #' content = i, #' media = img(src = "https://cdn.framework7.io/placeholder/fashion-88x88-1.jpg") #' ) #' }) #' ) #' }) #' #' observeEvent(input$insertBefore, { #' updateF7VirtualList( #' id = "vlist", #' action = "insertItemBefore", #' index = input$itemIndex, #' item = f7VirtualListItem( #' title = "New Item Title", #' content = "New Item Content", #' media = img(src = "https://cdn.framework7.io/placeholder/fashion-88x88-1.jpg") #' ) #' ) #' }) #' #' observeEvent(input$replaceItem, { #' updateF7VirtualList( #' id = "vlist", #' action = "replaceItem", #' index = input$itemIndex, #' item = f7VirtualListItem( #' title = "Replacement", #' content = "Replacement Content", #' media = img(src = "https://cdn.framework7.io/placeholder/fashion-88x88-1.jpg") #' ) #' ) #' }) #' #' observeEvent(input$deleteAllItems, { #' updateF7VirtualList( #' id = "vlist", #' action = "deleteAllItems" #' ) #' }) #' #' observeEvent(input$moveItem, { #' updateF7VirtualList( #' id = "vlist", #' action = "moveItem", #' oldIndex = input$itemIndex, #' newIndex = input$itemNewIndex #' ) #' }) #' #' observeEvent(input$filterItems, { #' updateF7VirtualList( #' id = "vlist", #' action = "filterItems", #' indexes = input$itemsFilter[1]:input$itemsFilter[2] #' ) #' }) #' #' } #' ) #' } updateF7VirtualList <- function(id, action = c("appendItem", "appendItems", "prependItem", "prependItems", "replaceItem", "replaceAllItems", "moveItem", "insertItemBefore", "filterItems", "deleteItem", "deleteAllItems", "scrollToItem"), item = NULL, items = NULL, index = NULL, indexes = NULL, oldIndex = NULL, newIndex = NULL, session = shiny::getDefaultReactiveDomain()) { # JavaScript starts from 0! index <- index - 1 indexes <- indexes - 1 oldIndex <- oldIndex - 1 newIndex <- newIndex - 1 message <- dropNulls( list( action = action, item = item, items = items, index = index, indexes = indexes, oldIndex = oldIndex, newIndex = newIndex ) ) session$sendInputMessage(inputId = id, message) }

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/code/mouse/R/gtf-munging.R

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nehiljain/pgi-analysis

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refs/heads/master

2021-01-10T21:06:34.180321

2015-04-29T17:48:06

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gtf-munging.R

rm(list=ls()) library(plyr) library(dplyr) library(stringr) # library(rattle) # This script loads the GTF file for mouse, creates a header, munges the strings to be made useful, converts to a dataframe and writes it to RData and CSV Files # change the path to point to the GTF file from ensemble ftp://ftp.ensembl.org/pub/release-77/gtf/mus_musculus # previewDf gives us idea about the structure of the file # Header is derived from http://uswest.ensembl.org/info/website/upload/gff.html # change the path to point to the GTF file from ensemble ftp://ftp.ensembl.org/pub/release-77/gtf/mus_musculus # WARNING: No gene name found for line 21995 to 22008 in mouse GTF file path and url above. Replaced with "NA" gtfFilePath <- "/home/data/reference/77/Mus_musculus.GRCm38.77.gtf" # previewDf <- read.table(file = gtfFilePath, # header = FALSE, # comment.char = "#", # nrow = 100, # na.strings = "NA", # fill = TRUE, # sep = "\t") headerName <- c("chromosome_name", "source", "feature", "start", "end", "score", "strand", "frame", "attribute") colClassNames <- c("character", "factor", "factor", "integer", "integer", "character", "character", "character", "character") gtfData <- read.table(file = gtfFilePath, header = FALSE, comment.char = "#", na.strings = "NA", fill = TRUE, sep = "\t", col.names = headerName) ## Reading is complete. ## Next we manipulate the attribute column to create 3 additional columns for gene id, gene name and gene biotype removeGeneNameTag <- function(row) { s <- as.character(row) # print(s) gene_name_loc <- str_locate(s, "gene_name ") if (!is.na(gene_name_loc[1])) { return(str_trim(str_sub(s, start = gene_name_loc[2]))) } # print(s) return(str_trim(s)) } removeGeneIdTag <- function(row) { s <- as.character(row) # print(s) gene_id_loc <- str_locate(s, "gene_id ") # print(gene_id_loc) if (!is.na(gene_id_loc[1])) { return(str_trim(str_sub(s, start = gene_id_loc[2]))) } return(str_trim(row)) } removeGeneBiotypeTag <- function(row) { s <- as.character(row) # print(s) gene_biotype_loc<- str_locate(s, "gene_biotype ") if (!is.na(gene_biotype_loc[1])) { return(str_trim(str_sub(s, start = gene_biotype_loc[2]))) } return(str_trim(s)) } splitAttributes <- str_split(gtfData$attribute, "; ") formattedAttributes <- ldply(splitAttributes, function (row) { id <- removeGeneIdTag(row[grep("gene_id", row)]) if ( length(grep("gene_name", row)) == 0 ) { g_name <- "NA" # this is done for line 21995 to 22008 in mouse GTF file path and url above. } else { g_name <- removeGeneNameTag(row[grep("gene_name", row)]) } biotype <- removeGeneBiotypeTag(row[grep("gene_biotype", row)]) print(g_name) df <- data.frame(gene_id = id, gene_name = g_name, gene_biotype = biotype) }) resultGtfData <- cbind(gtfData, formattedAttributes) str(resultGtfData) # names(resultGtfData) <- normVarNames(names(resultGtfData)) save(resultGtfData, file = "/home/data/reference/77/Mus_musculus.GRCm38.77.mouse_gtf.RData") write.csv(resultGtfData, file = "/home/data/reference/77/Mus_musculus.GRCm38.77.mouse_gtf.csv", quote = FALSE, na = "NA", row.names = FALSE) # # refGeneIdData <- read.csv(, file = "Downloads/mouse_gene_list-NCBIM37.67-mm9.txt", # header = TRUE, # comment.char = "#", # na.strings = "NA", # fill = TRUE) # # # names(refGeneIdData) <- normVarNames(names(refGeneIdData)) # save(refGeneIdData, file = "mouse_gene_list_mm9.RData") # write.csv(refGeneIdData, file = "mouse_gene_list_mm9.csv", # quote = FALSE, na = "NA", row.names = FALSE) # # write.csv(refGeneIdData[, 1], file = "test_mouse_gene_list_mm9.csv", # quote = FALSE, na = "NA", row.names = FALSE)

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/Laura_Pipeline/Clean_Lineage_genes.R

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tallulandrews/LiverTumouroidsScripts

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refs/heads/master

2022-03-02T18:07:38.553300

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Clean_Lineage_genes.R

# Lineage markers Chol_lineage <- read.table("/nfs/users/nfs_t/ta6/Collaborations/LiverOrganoids/Markers_130418_Chol.txt", header=TRUE) Hep_lineage <- read.table("/nfs/users/nfs_t/ta6/Collaborations/LiverOrganoids/Markers_130418_Hep.txt", header=TRUE) Hep_both <- Hep_lineage[ grepl("Prog", Hep_lineage[,2]) & grepl("Hep", Hep_lineage[,2]), 1] Chol_both <- Chol_lineage[ grepl("Prog", Chol_lineage[,2]) & grepl("Chol", Chol_lineage[,2]), 1] Prog_both <- Hep_lineage[ grepl("Prog", Hep_lineage[,2]) & Hep_lineage[,1] %in% Chol_lineage[Chol_lineage[,2] == "Prog",1], 1] Conflict1 <- Hep_lineage[ grepl("Hep", Hep_lineage[,2]) & Hep_lineage[,1] %in% Chol_lineage[Chol_lineage[,2] == "Chol",1], 1] Conflict2 <- Hep_lineage[ grepl("Prog", Hep_lineage[,2]) & Hep_lineage[,1] %in% Chol_lineage[Chol_lineage[,2] == "Chol",1], 1] Conflict3 <- Hep_lineage[ grepl("Hep", Hep_lineage[,2]) & Hep_lineage[,1] %in% Chol_lineage[Chol_lineage[,2] == "Prog",1], 1] Conflict4 <- Chol_lineage[ grepl("Prog", Chol_lineage[,2]) & Chol_lineage[,1] %in% Hep_lineage[Hep_lineage[,2] == "Hep",1], 1] Conflicts <- c(as.character(Conflict1), as.character(Conflict2), as.character(Conflict3), as.character(Conflicts4)) Chol_lineage <- Chol_lineage[Chol_lineage[,1] %in% marker_genes & Chol_lineage[,1] %in% keep_genes,] Hep_lineage <- Hep_lineage[Hep_lineage[,1] %in% marker_genes & Hep_lineage[,1] %in% keep_genes,] Chol_lineage[,2] <- as.character(Chol_lineage[,2]) Chol_lineage[Chol_lineage[,2] == "Prog",2] <- "Chol-Prog" Chol_lineage[Chol_lineage[,2] == "Chol",2] <- "Chol-Mature" Chol_lineage[Chol_lineage[,1] %in% Chol_both,2] <- "Chol-Both" Chol_lineage <- Chol_lineage[!(Chol_lineage[,1] %in% Conflicts),] Hep_lineage[,2] <- as.character(Hep_lineage[,2]) Hep_lineage[Hep_lineage[,2] == "Prog",2] <- "Hep-Prog" Hep_lineage[Hep_lineage[,2] == "Hep",2] <- "Hep-Mature" Hep_lineage[Hep_lineage[,1] %in% Hep_both,2] <- "Hep-Both" Hep_lineage <- Hep_lineage[!(Hep_lineage[,1] %in% Conflicts),] Lineage <- rbind(Chol_lineage,Hep_lineage) Lineage[Lineage[,1] %in% Prog_both,2] <- "Common-Prog" Lineage <- Lineage[!duplicated(Lineage[,1]),] Lineage[,1] <- as.character(Lineage[,1]) Lineage[Lineage[,1] == "05-Mar",1] <- "MARCH5" Lineage <- unique(Lineage) write.table(Lineage, file="Cleaned_Lineage.txt", row.names=F, col.names=F)

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/FindFeaturesFrom PCa.R

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WeichselRiver/stamps

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refs/heads/master

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2020-01-25T08:05:30

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FindFeaturesFrom PCa.R

# Stamp Recognition using PCA #.libPaths(c("C:/Daten/RStudio/R-3.3.2/library", "C:/Daten/R-3.1.2/library" )) library(EBImage) library(dplyr) library(readxl) t1 = read_excel("StampList.xlsx") %>% dplyr::filter(bild == "Ziffern im Kreis") pic_prep = function(x) { x1 = EBImage::readImage(x) %>% resize(w = 100, h = 100) colorMode(x1) = "Grayscale" as.vector(imageData(x1[,,1])) } # get data.frame of all pictures pics_array = plyr::laply(t1$file, pic_prep) %>% t %>% data.frame pca = princomp(pics_array) # rescale function linMap <- function(x, from = 0, to = 1) (x - min(x)) / max(x - min(x)) * (to - from) + from pca_pic = pca$scores[,1] %>% linMap %>% Image(dim = c(100,100), colormode = "Grayscale") display(pca_pic, method = "raster") # find features bwlabel(pca_pic) computeFeatures.shape(pca_pic) ## load and segment nucleus y = pca_pic x = thresh(y, 10, 10, 0.05) # x = opening(x, makeBrush(5, shape='disc')) x = bwlabel(x) display(y, title="Cell nuclei", method = "raster") display(x, title="Segmented nuclei", method = "raster") ## compute shape features fts = computeFeatures.shape(x) data.frame(fts) %>% dplyr::arrange(desc(s.area)) ## compute features ft = computeFeatures(x, y, xname="nucleus") cat("median features are:\n") apply(ft, 2, median) ## compute feature properties ftp = computeFeatures(x, y, properties=TRUE, xname="nucleus") ftp cx = colorLabels(x) display(cx, method = "raster")

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/dmeas.R

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dkenned1/KennedyDunnRead

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refs/heads/master

2020-12-30T09:11:41.294172

2017-08-15T17:04:47

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dmeas.R

dmeas <- Csnippet(" double tol=1.0e-17; double DetectionLimit=2; double probit_beta_0= -2.206; double probit_beta_1= 1.555; double vcn_beta_0= 1.127; double vcn_beta_1= -0.151; double lVirusConc = log10(V/D +1); double ProbitValue=probit_beta_0 + probit_beta_1*lVirusConc; double dNormValue=vcn_beta_0 + vcn_beta_1 *lVirusConc; if (log10(VCN1+1)<DetectionLimit) { lik = pnorm(ProbitValue,0,1,0,1); //prob that virus is undetectable //last argument is log=TRUE, second to last is lower.tail=FALSE } else { lik = pnorm(ProbitValue,0,1,1,1); //prob that virus is detectable lik += dnorm(log10(VCN1+1), lVirusConc, dNormValue, 1); //prob that virus is exactly equal to data } if (Ndata>1) { if (log10(VCN2+1)<DetectionLimit) { lik += pnorm(ProbitValue,0,1,0,1); } else { lik += pnorm(ProbitValue,0,1,1,1); lik += dnorm(log10(VCN2+1), lVirusConc, dNormValue, 1); } if (Ndata>2) { if (log10(VCN3+1)<DetectionLimit) { lik += pnorm(ProbitValue,0,1,0,1); } else { lik += pnorm(ProbitValue,0,1,1,1); lik += dnorm(log10(VCN3+1), lVirusConc, dNormValue, 1); } if (Ndata>3) { if (log10(VCN4+1)<DetectionLimit) { lik += pnorm(ProbitValue,0,1,0,1); } else { lik += pnorm(ProbitValue,0,1,1,1); lik += dnorm(log10(VCN4+1), lVirusConc, dNormValue, 1); } if (Ndata>4) { if (log10(VCN5+1)<DetectionLimit) { lik += pnorm(ProbitValue,0,1,0,1); } else { lik += pnorm(ProbitValue,0,1,1,1); lik += dnorm(log10(VCN5+1), lVirusConc, dNormValue, 1); } if (Ndata>5) { if (log10(VCN6+1)<DetectionLimit) { lik += pnorm(ProbitValue,0,1,0,1); } else { lik += pnorm(ProbitValue,0,1,1,1); lik += dnorm(log10(VCN6+1), lVirusConc, dNormValue, 1); } } } } } } if(isnan(lik)) { //Rprintf(\"%lg\\n\",lik); lik=-300; } lik=exp(lik) + tol; ")

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/weatherType2Table.R

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drmingle/Wallmart-II

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refs/heads/master

2021-01-15T13:13:35.495976

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weatherType2Table.R

weatherType2Table <- function(weatherSting){ types2check <- c("TS", "GR", "RA", "DZ", "SN", "SG", "GS", "PL", "FG", "BR", "UP", "HZ", "FU", "DU", "SS", "SQ", "FZ", "MI", "PR", "BC", "BL", "VC") logicalString <- sapply(types2check, function(type){ return(grepl(type, weatherSting)) }) return(logicalString) }

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/fuzzedpackages/gaston/man/lik.contour.Rd

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akhikolla/testpackages

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refs/heads/master

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lik.contour.Rd

\name{lik.contour} \alias{lik.contour} \title{ Contour plot for two parameters likelihood } \description{ Create a contour plot (superimposed with a heat map) } \usage{ lik.contour(x, y, z, levels = NULL, nlevels = 11, heat = TRUE, col.heat = NULL, ...) } \arguments{ \item{x, y, z}{ As in \code{contour} } \item{levels}{ As in \code{contour}. If \code{NULL}, the function computes appropriate levels. } \item{nlevels}{ As in \code{contour} } \item{heat}{ If \code{TRUE}, a heat map is superimposed to the contour plot } \item{col.heat}{ Vector of heat colors} \item{\dots}{ Additional arguments to \code{image} and \code{contour}} } \details{ This function is a wrapper for \code{contour}, with a different method to compute a default value for levels. If \code{heat = TRUE}, a heatmap produced by \code{image} is added to the plot. See \code{\link{contour}} for details on parameters. } \author{ Hervé Perdry and Claire Dandine-Roulland } \seealso{ \code{\link{lmm.diago.likelihood}}, \code{\link[graphics:contour]{contour}}, \code{\link[graphics:image]{image}} } \examples{ data(AGT) x <- as.bed.matrix(AGT.gen, AGT.fam, AGT.bim) # Compute Genetic Relationship Matrix K <- GRM(x) # eigen decomposition of K eiK <- eigen(K) # simulate a phenotype set.seed(1) y <- 1 + lmm.simu(tau = 1, sigma2 = 2, eigenK = eiK)$y # Likelihood TAU <- seq(0.5,2.5,length=30) S2 <- seq(1,3,length=30) lik1 <- lmm.diago.likelihood(tau = TAU, s2 = S2, Y = y, eigenK = eiK) lik.contour(TAU, S2, lik1, heat = TRUE, xlab = "tau", ylab = "sigma^2") } \keyword{ Heat map }

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/data/genthat_extracted_code/rms/examples/orm.fit.Rd.R

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orm.fit.Rd.R

library(rms) ### Name: orm.fit ### Title: Ordinal Regression Model Fitter ### Aliases: orm.fit ### Keywords: models regression ### ** Examples #Fit an additive logistic model containing numeric predictors age, #blood.pressure, and sex, assumed to be already properly coded and #transformed # # fit <- orm.fit(cbind(age,blood.pressure,sex), death)

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/preprocessing/sample-data-test.R

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jodeleeuw/219-2020-eeg-analysis

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refs/heads/master

2023-07-27T06:40:57.453364

2021-09-13T12:21:17

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sample-data-test.R

library(readr) library(dplyr) test_data <- read_csv('data/behavioral/longtest_csv.csv') trials <- test_data %>% filter(phase=="test") %>% group_by(audio_type, match_type) %>% summarize(n=n()) trials <- test_data %>% filter(phase=="test") %>% group_by(audio_type, match_type, stimulus) %>% summarize(n=n()) trials <- test_data %>% filter(phase=="test") %>% group_by(image_category, sound_category) %>% summarize(n=n())

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/algorithm/iterative_cmi_greedy_flexible_parallel.R

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ylincen/CMI-adaptive-hist

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iterative_cmi_greedy_flexible_parallel.R

iterative_cmi_greedy_flexible_parallel = function(data, eps = 0, Kmax = 0, v=F, isCat = c(), max_num_updating = 20, cores=1){ require("parallel") cores = min(cores, detectCores()) ## check if number of available cores is sufficient # transform the categorical data into 1,2,3,...,(number of categories) if(length(isCat)>0){ data[,isCat] = apply(data[,isCat,drop = F],2, function(x){as.integer(as.factor(x))}) } # In case it is 1D data, then no iteration is needed. if(ncol(data) == 1){ if(length(isCat) == 1){ res = multi_hist_splitting_seed_based_simple(data, isCat = T) } else { res = multi_hist_splitting_seed_based_simple(data) } return(res) } # initialize the grid prev_res = NULL for(i in 1:ncol(data)){ if(i %in% isCat){ res = multi_hist_splitting_seed_based_simple(data[,i,drop=F], res = prev_res, isCat = T) } else { res = multi_hist_splitting_seed_based_simple(data[,i,drop=F], res = prev_res, Kmax = 1) } prev_res = res } l1 = res$L1 r1 = res$R1 if(length(res$L1) > 1){ l1 = res$L1[1] r1 = res$R1[1] } # iteratively updating each dimension in a greedy manner min_sc = res$L[1] + res$R[1] min_res = NULL min_dim = 1:ncol(data) remaining_cat = isCat dims_in_order = 1:ncol(data) for(iter in 1:max_num_updating){ min_dim_here = 0 num_updates = 0 result_list = mclapply(1:(ncol(data)), function(jj){ # drop the current split of this dimension dim = dims_in_order[jj] if(dim %in% isCat){ return(NULL) } sub_cols_to_remove = c(2*jj-1, 2*jj) sub_cols = setdiff(1:(2*ncol(data)), sub_cols_to_remove) duplicated_info_list = prev_res[,sub_cols] %>% as.matrix() duplicated_info_list = duplicated_row_indices(duplicated_info_list) # The function duplicated_row_indices() is in utils.R if(length(duplicated_info_list$ind_dup) == 0){ prev_res_drop = prev_res[,-sub_cols_to_remove,drop=F] # no duplicated rows to remove } else{ prev_res_drop = prev_res[duplicated_info_list$ind_base,-sub_cols_to_remove, drop=F] # the result if we remove the split of one dimension for(i in 1:length(duplicated_info_list$ind_dup)){ prev_res_drop[duplicated_info_list$corresponding_ind[i],"local_index"][[1]] = c(unlist(prev_res_drop[duplicated_info_list$corresponding_ind[i],"local_index"]), unlist(prev_res[duplicated_info_list$ind_dup[i],"local_index"])) %>% list() } } # update res = multi_hist_splitting_seed_based_simple(data[,dim,drop=F], res = prev_res_drop, isCat = dim %in% isCat, Kmax = Kmax) return(res) }, mc.cores=cores) for(jj in 1:(ncol(data))){ dim = dims_in_order[jj] if(dim %in% isCat){ next } num_updates = num_updates + 1 res = ((result_list[jj]))[[1]] if(res$L[1] + res$R[1] < min_sc){ # note that min_sc is not equal to min_sc if we are considering a categorical dimension min_res = res # update min_res min_sc = res$L[1] + res$R[1] # update min_sc min_dim_here = dim } } if(min_dim_here == 0){ # break if further split will not have lower SC break } min_dim = c(min_dim, min_dim_here) # update the min_dim if(min_dim_here %in% isCat){ remaining_cat = setdiff(remaining_cat, min_dim_here) } prev_res = min_res # update the prev_res dims_in_order = c(setdiff(dims_in_order, min_dim_here),min_dim_here) if(num_updates <= 1){ break } } res = prev_res # make the res in the right order min_dim = unique(min_dim, fromLast = T) correct_order = c(min_dim * 2 - 1, min_dim * 2) %>% matrix(byrow = T, nrow = 2) %>% as.numeric() res[,correct_order] = res[,1:(2*ncol(data))] res$L1 = l1 res$R1 = r1 return(res) }

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/R/est_lucid.R

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est_lucid.R

#' @title Fit LUCID model to conduct integrated clustering #' #' @description The Latent Unknown Clustering with Integrated Data (LUCID) performs #' integrative clustering using multi-view data. LUCID model is estimated via EM #' algorithm for model-based clustering. It also features variable selection, #' integrated imputation, bootstrap inference and visualization via Sankey diagram. #' #' @param G Exposures, a numeric vector, matrix, or data frame. Categorical variable #' should be transformed into dummy variables. If a matrix or data frame, rows #' represent observations and columns correspond to variables. #' @param Z Omics data, a numeric matrix or data frame. Rows correspond to observations #' and columns correspond to variables. #' @param Y Outcome, a numeric vector. Categorical variable is not allowed. Binary #' outcome should be coded as 0 and 1. #' @param CoG Optional, covariates to be adjusted for estimating the latent cluster. #' A numeric vector, matrix or data frame. Categorical variable should be transformed #' into dummy variables. #' @param CoY Optional, covariates to be adjusted for estimating the association #' between latent cluster and the outcome. A numeric vector, matrix or data frame. #' Categorical variable should be transformed into dummy variables. #' @param K Number of latent clusters. An integer greater or equal to 2. User #' can use \code{\link{lucid}} to determine the optimal number of latent clusters. #' @param family Distribution of outcome. For continuous outcome, use "normal"; #' for binary outcome, use "binary". Default is "normal". #' @param useY Flag to include information of outcome when estimating the latent #' cluster. Default is TRUE. #' @param tol Tolerance for convergence of EM algorithm. Default is 1e-3. #' @param max_itr Max number of iterations for EM algorithm. #' @param max_tot.itr Max number of total iterations for \code{est_lucid} function. #' \code{est_lucid} may conduct EM algorithm for multiple times if the algorithm #' fails to converge. #' @param Rho_G A scalar. This parameter is the LASSO penalty to regularize #' exposures. If user wants to tune the penalty, use the wrapper #' function \code{lucid} #' @param Rho_Z_Mu A scalar. This parameter is the LASSO penalty to #' regularize cluster-specific means for omics data (Z). If user wants to tune the #' penalty, use the wrapper function \code{lucid} #' @param Rho_Z_Cov A scalar. This parameter is the graphical LASSO #' penalty to estimate sparse cluster-specific variance-covariance matrices for omics #' data (Z). If user wants to tune the penalty, use the wrapper function \code{lucid} #' @param modelName The variance-covariance structure for omics data. #' See \code{mclust::mclustModelNames} for details. #' @param seed An integer to initialize the EM algorithm or imputing missing values. #'Default is 123. #' @param init_impute Method to initialize the imputation of missing values in #' LUCID. "mclust" will use \code{mclust:imputeData} to implement EM Algorithm #' for Unrestricted General Location Model to impute the missing values in omics #' data; \code{lod} will initialize the imputation via relacing missing values by #' LOD / sqrt(2). LOD is determined by the minimum of each variable in omics data. #' @param init_par Method to initialize the EM algorithm. "mclust" will use mclust #' model to initialize parameters; "random" initialize parameters from uniform #' distribution. #' @param verbose A flag indicates whether detailed information for each iteration #' of EM algorithm is printed in console. Default is FALSE. #' #' #' #' @return A list which contains the several features of LUCID, including: #' \item{pars}{Estimates of parameters of LUCID, including beta (effect of #' exposure), mu (cluster-specific mean for omics data), sigma (cluster-specific #' variance-covariance matrix for omics data) and gamma (effect estimate of association #' between latent cluster and outcome)} #' \item{K}{Number of latent cluster} #' \item{modelName}{Geometric model to estiamte variance-covariance matrix for #' omics data} #' \item{likelihood}{The log likelihood of the LUCID model} #' \item{post.p}{Posterior inclusion probability (PIP) for assigning observation i #' to latent cluster j} #' \item{Z}{If missing values are observed, this is the complet dataset for omics #' data with missing values imputed by LUCID} #' #' @importFrom nnet multinom #' @import mclust #' @importFrom glmnet glmnet #' @importFrom glasso glasso #' @import stats #' @import utils #' @import mix #' @export #' #' @references #' Cheng Peng, Jun Wang, Isaac Asante, Stan Louie, Ran Jin, Lida Chatzi, #' Graham Casey, Duncan C Thomas, David V Conti, A Latent Unknown Clustering #' Integrating Multi-Omics Data (LUCID) with Phenotypic Traits, Bioinformatics, #' btz667, https://doi.org/10.1093/bioinformatics/btz667. #' #' #' @examples #' \dontrun{ #' # use simulated data #' G <- sim_data$G #' Z <- sim_data$Z #' Y_normal <- sim_data$Y_normal #' Y_binary <- sim_data$Y_binary #' cov <- sim_data$Covariate #' #' # fit LUCID model with continuous outcome #' fit1 <- est_lucid(G = G, Z = Z, Y = Y_normal, family = "normal", K = 2, #' seed = 1008) #' #' # fit LUCID model with block-wise missing pattern in omics data #' Z_miss_1 <- Z #' Z_miss_1[sample(1:nrow(Z), 0.3 * nrow(Z)), ] <- NA #' fit2 <- est_lucid(G = G, Z = Z_miss_1, Y = Y_normal, family = "normal", K = 2) #' #' # fit LUCID model with sporadic missing pattern in omics data #' Z_miss_2 <- Z #' index <- arrayInd(sample(length(Z_miss_2), 0.3 * length(Z_miss_2)), dim(Z_miss_2)) #' Z_miss_2[index] <- NA #' # initialize imputation by imputing #' fit3 <- est_lucid(G = G, Z = Z_miss_2, Y = Y_normal, family = "normal", #' K = 2, seed = 1008, init_impute = "lod") #' LOD #' # initialize imputation by mclust #' fit4 <- est_lucid(G = G, Z = Z_miss_2, Y = Y, family = "normal", K = 2, #' seed = 123, init_impute = "mclust") #' #' # fit LUCID model with binary outcome #' fit5 <- est_lucid(G = G, Z = Z, Y = Y_binary, family = "binary", K = 2, #' seed = 1008) #' #' # fit LUCID model with covariates #' fit6 <- est_lucid(G = G, Z = Z, Y = Y_binary, CoY = cov, family = "binary", #' K = 2, seed = 1008) #' #' # use LUCID model to conduct integrated variable selection #' # select exposure #' fit6 <- est_lucid(G = G, Z = Z, Y = Y_normal, CoY = NULL, family = "normal", #' K = 2, seed = 1008, Rho_G = 0.1) #' # select omics data #' fit7 <- est_lucid(G = G, Z = Z, Y = Y_normal, CoY = NULL, family = "normal", #' K = 2, seed = 1008, Rho_Z_Mu = 90, Rho_Z_Cov = 0.1, init_par = "random") #' #' } est_lucid <- function(G, Z, Y, CoG = NULL, CoY = NULL, K = 2, family = c("normal", "binary"), useY = TRUE, tol = 1e-3, max_itr = 1e3, max_tot.itr = 1e4, Rho_G = 0, Rho_Z_Mu = 0, Rho_Z_Cov = 0, modelName = NULL, seed = 123, init_impute = c("mclust", "lod"), init_par = c("mclust", "random"), verbose = FALSE) { # 1. basic setup for estimation function ============= family <- match.arg(family) init_impute <- match.arg(init_impute) init_par <- match.arg(init_par) Select_G <- FALSE Select_Z <- FALSE if(Rho_G != 0) { Select_G <- TRUE } if(Rho_Z_Mu != 0 | Rho_Z_Cov != 0) { Select_Z <- TRUE } ## 1.1 check data format ==== if(is.null(G)) { stop("Input data 'G' is missing") } else { if(!is.matrix(G)) { G <- as.matrix(G) if(!is.numeric(G)) { stop("Input data 'G' should be numeric; categorical variables should be transformed into dummies") } } } if(is.null(colnames(G))){ Gnames <- paste0("G", 1:ncol(G)) } else { Gnames <- colnames(G) } colnames(G) <- Gnames if(is.null(Z)) { stop("Input data 'Z' is missing") } else { if(!is.matrix(Z)) { Z <- as.matrix(Z) if(!is.numeric(Z)) { stop("Input data 'Z' should be numeric") } } } if(is.null(colnames(Z))){ Znames <- paste0("Z", 1:ncol(Z)) } else { Znames <- colnames(Z) } if(is.null(Y)) { stop("Input data 'Y' is missing") } else { if(!is.matrix(Y)) { Y <- as.matrix(Y) if(!is.numeric(Y)) { stop("Input data 'Y' should be numeric; binary outcome should be transformed them into dummies") } if(ncol(Y) > 1) { stop("Only continuous 'Y' or binary 'Y' is accepted") } } } if(is.null(colnames(Y))) { Ynames <- "outcome" } else { Ynames <- colnames(Y) } colnames(Y) <- Ynames if(family == "binary") { if(!(all(Y %in% c(0, 1)))) { stop("Binary outcome should be coded as 0 and 1") } } CoGnames <- NULL if(!is.null(CoG)) { if(!is.matrix(CoG)) { CoG <- as.matrix(CoG) if(!is.numeric(CoG)) { stop("Input data 'CoG' should be numeric; categroical variables should be transformed into dummies") } } if(is.null(colnames(CoG))) { CoGnames <- paste0("CoG", 1:ncol(CoG)) } else { CoGnames <- colnames(CoG) } colnames(CoG) <- CoGnames } CoYnames <- NULL if(!is.null(CoY)) { if(!is.matrix(CoY)) { CoY <- as.matrix(CoY) if(!is.numeric(CoY)) { stop("Input data 'CoY' should be numeric; categorical variables should be transformed into dummies") } } if(is.null(colnames(CoY))) { CoYnames <- paste0("CoY", 1:ncol(CoY)) } else { CoYnames <- colnames(CoY) } colnames(CoY) <- CoYnames } ## 1.2 record input dimensions, family function ==== N <- nrow(Y) dimG <- ncol(G) dimZ <- ncol(Z); dimCoG <- ifelse(is.null(CoG), 0, ncol(CoG)) dimCoY <- ifelse(is.null(CoY), 0, ncol(CoY)) G <- cbind(G, CoG) Gnames <- c(Gnames, CoGnames) family.list <- switch(family, normal = normal(K = K, dimCoY), binary = binary(K = K, dimCoY)) Mstep_Y <- family.list$f.maxY switch_Y <- family.list$f.switch ## 1.3. check missing pattern ==== na_pattern <- check_na(Z) if(na_pattern$impute_flag) { # initialize imputation if(init_impute == "mclust") { cat("Intializing imputation of missing values in 'Z' via the mix package \n\n") invisible(capture.output(Z <- mclust::imputeData(Z, seed = seed))) Z[na_pattern$indicator_na == 3, ] <- NA } if(init_impute == "lod") { cat("Intializing imputation of missing values in 'Z' via LOD / sqrt(2) \n\n") Z <- apply(Z, 2, fill_data_lod) colnames(Z) <- Znames } } # 2. EM algorithm for LUCID ================ tot.itr <- 0 convergence <- FALSE while(!convergence && tot.itr <= max_tot.itr) { if(tot.itr > 0) { seed <- seed + 10 } set.seed(seed) ## 2.1 initialize model parameters ==== # initialize beta res.beta <- matrix(data = runif(K * (dimG + dimCoG + 1)), nrow = K) res.beta[1, ] <- 0 # initialize mu and sigma # initialize by mclust if(init_par == "mclust") { cat("Initialize LUCID with mclust \n") invisible(capture.output(mclust.fit <- Mclust(Z[na_pattern$indicator_na != 3, ], G = K, modelNames = modelName))) if(is.null(mclust.fit)) { stop("mclust failed for specified model - please set modelName to `NULL` to conduct automatic model selection ") } if(is.null(modelName)){ model.best <- mclust.fit$modelName } else{ model.best <- modelName } res.mu <- t(mclust.fit$parameters$mean) res.sigma <- mclust.fit$parameters$variance$sigma # browser() } else { # initialize by random guess cat("Initialize LUCID with random values from uniform distribution \n") if(is.null(modelName)){ model.best <- "VVV" cat("GMM model for LUCID is not specified, 'VVV' model is used by default \n") } else{ model.best <- modelName } res.mu <- matrix(runif(dimZ * K, min = -0.5, max = 0.5), nrow = K) res.sigma <- gen_cov_matrices(dimZ = dimZ, K = K) } # initialize family specific parameters gamma res.gamma <- family.list$initial.gamma(K, dimCoY) # start EM algorithm cat("Fitting LUCID model", paste0("K = ", K, ", Rho_G = ", Rho_G, ", Rho_Z_Mu = ", Rho_Z_Mu, ", Rho_Z_Cov = ", Rho_Z_Cov, ")"), "\n") res.loglik <- -Inf itr <- 0 while(!convergence && itr <= max_itr){ itr <- itr + 1 tot.itr <- tot.itr + 1 check.gamma <- TRUE # 2.2 E-step ==== # calculate log-likelihood for observation i being assigned to cluster j new.likelihood <- Estep(beta = res.beta, mu = res.mu, sigma = res.sigma, gamma = res.gamma, G = G, Z = Z, Y = Y, CoY = CoY, N = N, K = K, family.list = family.list, itr = itr, useY = useY, dimCoY = dimCoY, ind.na = na_pattern$indicator_na) # normalize the log-likelihood to probability res.r <- t(apply(new.likelihood, 1, lse_vec)) if(!all(is.finite(res.r))){ cat("iteration", itr,": EM algorithm collapsed: invalid estiamtes due to over/underflow, try LUCID with another seed \n") break } else{ if(isTRUE(verbose)) { cat("iteration", itr,": E-step finished.\n") } } # 2.3 M-step - parameters ==== # update model parameters to maximize the expected likelihood invisible(capture.output(new.beta <- Mstep_G(G = G, r = res.r, selectG = Select_G, penalty = Rho_G, dimG = dimG, dimCoG = dimCoG, K = K))) new.mu.sigma <- Mstep_Z(Z = Z, r = res.r, selectZ = Select_Z, penalty.mu = Rho_Z_Mu, penalty.cov = Rho_Z_Cov, model.name = model.best, K = K, ind.na = na_pattern$indicator_na, mu = res.mu) if(is.null(new.mu.sigma$mu)){ cat("variable selection failed, try LUCID with another seed \n") break } if(useY){ new.gamma <- Mstep_Y(Y = Y, r = res.r, CoY = CoY, K = K, CoYnames) check.gamma <- is.finite(unlist(new.gamma)) } # 2.4 M step - impute missing values ==== if(na_pattern$impute_flag){ Z <- Istep_Z(Z = Z, p = res.r, mu = res.mu, sigma = res.sigma, index = na_pattern$index) } # 2.5 control step ==== check.value <- all(is.finite(new.beta), is.finite(unlist(new.mu.sigma)), check.gamma) if(!check.value){ cat("iteration", itr,": Invalid estimates, try LUCID with another seed \n") break } else{ res.beta <- new.beta res.mu <- new.mu.sigma$mu res.sigma <- new.mu.sigma$sigma if(useY){ res.gamma <- new.gamma } new.loglik <- sum(rowSums(res.r * new.likelihood)) if(Select_G) { new.loglik <- new.loglik - Rho_G * sum(abs(res.beta)) } if(Select_Z) { new.loglik <- new.loglik - Rho_Z_Mu * sum(abs(res.mu)) - Rho_Z_Cov * sum(abs(res.sigma)) } if(isTRUE(verbose)) { if(Select_G | Select_Z) { cat("iteration", itr,": M-step finished, ", "penalized loglike = ", sprintf("%.3f", new.loglik), "\n") } else{ cat("iteration", itr,": M-step finished, ", "loglike = ", sprintf("%.3f", new.loglik), "\n") } } else { cat(".") } if(abs(res.loglik - new.loglik) < tol){ convergence <- TRUE cat("Success: LUCID converges!", "\n\n") } res.loglik <- new.loglik } } } # 3. summarize results =============== if(!useY){ res.gamma <- Mstep_Y(Y = Y, r = res.r, CoY = CoY, K = K, CoYnames = CoYnames) } res.likelihood <- Estep(beta = res.beta, mu = res.mu, sigma = res.sigma, gamma = res.gamma, G = G, Z = Z, Y = Y, family.list = family.list, itr = itr, CoY = CoY, N = N, K = K, dimCoY = dimCoY, useY = useY, ind.na = na_pattern$indicator_na) res.r <- t(apply(res.likelihood, 1, lse_vec)) res.loglik <- sum(rowSums(res.r * res.likelihood)) if(Select_G) { res.loglik <- res.loglik - Rho_G * sum(abs(res.beta)) } if(Select_Z) { res.loglik <- res.loglik - Rho_Z_Mu * sum(abs(res.mu)) - Rho_Z_Cov * sum(abs(res.sigma)) } # browser() pars <- switch_Y(beta = res.beta, mu = res.mu, sigma = res.sigma, gamma = res.gamma, K = K) res.r <- res.r[, pars$index] colnames(pars$beta) <- c("intercept", Gnames) colnames(pars$mu) <- Znames if(Select_G){ tt1 <- apply(pars$beta[, -1], 2, range) selectG <- abs(tt1[2, ] - tt1[1, ]) > 0.001 } else{ selectG <- rep(TRUE, dimG) } if(Select_Z){ tt2 <- apply(pars$mu, 2, range) selectZ <- abs(tt2[2, ] - tt2[1, ]) > 0.001 } else{ selectZ <- rep(TRUE, dimZ) } results <- list(pars = list(beta = pars$beta, mu = pars$mu, sigma = pars$sigma, gamma = pars$gamma), K = K, var.names =list(Gnames = Gnames, Znames = Znames, Ynames = Ynames), modelName = model.best, likelihood = res.loglik, post.p = res.r, family = family, select = list(selectG = selectG, selectZ = selectZ), useY = useY, Z = Z, init_impute = init_impute, init_par = init_par, Rho = list(Rho_G = Rho_G, Rho_Z_Mu = Rho_Z_Mu, Rho_Z_Cov = Rho_Z_Cov) ) class(results) <- c("lucid") return(results) }

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#Reading in the data data = read.table("household_power_consumption.txt", sep=";",header=TRUE, row.names=NULL,na.strings="?") #Formatting the data field data$Date <- as.Date( as.character(data$Date), "%d/%m/%Y") #Subsetting the data subsetdata <- subset(data, Date >= as.Date("2007-02-01") & Date <= as.Date("2007-02-02")) #Removing all NA from data frame na.omit(subsetdata) #Plots histogram to screen hist(subsetdata$Global_active_power,col = "red", main ="Global Active Power",xlab = "Global Active Power (killowatts)", ylab="Frequency",breaks=12) #Copying image displayed on screen to png file dev.copy(png,file="plot1.png") #Closing device dev.off()

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## Do we have permission to download files? (NOT EXPORTED) ## @param destdir character value giving directory into which to download files ## @return logical value indicating whether this user has permission to download fies to ~/data/argo canDownload <- function(destdir="~/data/argo") { ## FIXME(dek): add username for @harbinj in next line (also, I was guessing on @richardsc's linux name) isDeveloper <- Sys.getenv("USER") == "kelley" || Sys.getenv("USER") == "jaimieharbin" || Sys.getenv("USER") == "richardsc" canWrite <- file.exists(destdir) && file.info(destdir)$isdir isDeveloper && canWrite }

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#' Ask questions in the interactive environment #' #' This function will print the questions in the interactive learning environment. #' #' @details #' This function reads the selected dataset and print the first row of its first #' colomn, i.e. the question. Then it presents to the user a menu, which gives him #' multiple choices. According to the choice made by the user, the function gives a #' score point and will return a due date, inspired by the SuperMemo-2 and Anki algorithms. #' The menu also proposes to show the answer (the 2nd column of the row), to give a #' hint/example, or to go back to the main menu. Finally, the function reorders the dataset #' in order to get the lower points score in its first row and return the function once again. #' #' @note #' In order to quit, simply type 0. #' #' @param assign.env An environment #' #' @importFrom utils menu read.csv select.list write.csv browseURL #' @importFrom magick image_read image_scale #' #' @source \url{https://www.supermemo.com/english/ol/sm2.htm}{ SuperMemo-2 algorithm} #' @source \url{https://apps.ankiweb.net/docs/manual.html#what-spaced-repetition-algorithm-does-anki-use}{ Anki algorithm} sessionQuestions <- function(assign.env = parent.frame(1)) { sessionDataset <- read.csv(paste0("", datasetAbsolutePath,""), stringsAsFactors = FALSE) # order dataset by dueDate and Score #sessionDataset <- sessionDataset[order(sessionDataset$dueDate, sessionDataset$Score), ] #assign("sessionDataset", sessionDataset, envir = assign.env) # check if rows to learn for current session and print question if(as.Date(sessionDataset$dueDate[1]) <= as.Date(Sys.Date())) { ## list image extension image_ext <- c(".jpg", ".JPG", ".jpeg", ".JPEG", ".png", ".PNG", ".svg", ".SVG", ".gif", ".GIF", ".avi", ".AVI", ".ico", ".ICO", ".icon", ".ICON", ".tiff", ".TIFF") if(any(sapply(image_ext, function(x) grepl(x, sessionDataset[1,1], fixed = TRUE)))) { message(paste("| Question: [see image]")) ## PRINT IMAGE image1 <- tryCatch(magick::image_read(sessionDataset[1,1]), error = function(e) paste0("Could not read image at ", sessionDataset[1,1])) image1 <- tryCatch(magick::image_scale(image1, "x300"), error = function(e) paste0("Could not read image at ", sessionDataset[1,1])) print(image1, info = FALSE) } else { message(paste("| Question:", sessionDataset[1,1],"")) } } else { message(paste("| 0 row to learn... Back to menu. \n")) return(learn()) } # menu 1, inspired by Anki app # ref: https://apps.ankiweb.net/ switch(menu(c("Show answer", "Hint", paste0("Back to menu (",length(which(sessionDataset$dueDate <= as.Date(Sys.Date())))," left to learn)"))) + 1, return(sessionExit()), # "Show answer" ## list image extension if(any(sapply(c(".jpg", ".JPG", ".jpeg", ".JPEG", ".png", ".PNG", ".svg", ".SVG", ".gif", ".GIF", ".avi", ".AVI", ".ico", ".ICO", ".icon", ".ICON", ".tiff", ".TIFF"), function(x) grepl(x, sessionDataset[1,2], fixed = TRUE)))) { message(paste("| Answer: [see image]")) ## PRINT IMAGE image2 <- tryCatch(magick::image_read(sessionDataset[1,2]), error = function(e) paste0("Could not read image at ", sessionDataset[1,2])) image2 <- tryCatch(magick::image_scale(image2, "x300"), error = function(e) paste0("Could not scale image at ", sessionDataset[1,2])) print(image2, info = FALSE) } else { message(paste("| Answer:", sessionDataset[1,2],"")) }, # "Hint/Example" if (names(sessionDataset[3]) != "Score") { if(any(sapply(c(".jpg", ".JPG", ".jpeg", ".JPEG", ".png", ".PNG", ".svg", ".SVG", ".gif", ".GIF", ".avi", ".AVI", ".ico", ".ICO", ".icon", ".ICON", ".tiff", ".TIFF"), function(x) grepl(x, sessionDataset[1,3], fixed = TRUE)))) { message(paste("| Hint: [see image]")) ## If image, open in default browse ## Not in viewer because can overwrite image of Question or Answer utils::browseURL(sessionDataset[1,3]) return(sessionQuestions()) } else { message(paste("| Hint:", sessionDataset[1,3],"")) return(sessionQuestions()) } } else { message(paste("| No Hint in this dataset.")) return(sessionQuestions()) }, return(learn())) # space repetition learning algorithm, inspired by SuperMemo 2. switch(menu(c("Hard", "Good", if(sessionDataset$Repetition[1] == 0){ paste0("Easy (+1 day)")} else if(sessionDataset$Repetition[1] == 1){ paste0("Easy (+4 days)")} else if(sessionDataset$Repetition[1] > 1){paste0("Easy (+", (sessionDataset$Interval[[1]] - 1)*max(1.3, sessionDataset$eFactor[[1]]+(0.1-(5-5)*(0.08+(5-5)*0.02))), " days)")} else{ paste0("Easy")})) + 1, return(sessionExit()), # "Hard" (fail and again) if(exists("sessionDataset")) { sessionDataset$Score[1] <- sessionDataset$Score[1] + 1 assign("sessionDataset", sessionDataset, envir = assign.env) sessionDataset$eFactor[1] <- 2.5 #default eFactor assign("sessionDataset", sessionDataset, envir = assign.env) sessionDataset$Interval[1] <- as.difftime(0, units = "days") #0 day interval assign("sessionDataset", sessionDataset, envir = assign.env) }, # "Good" (again) if(exists("sessionDataset")) { sessionDataset$Score[1] <- sessionDataset$Score[1] + 2 assign("sessionDataset", sessionDataset, envir = assign.env) }, # "Easy" (pass) if(sessionDataset$Repetition[1] == 0) { sessionDataset$Repetition[1] <- sessionDataset$Repetition[1] + 1 assign("sessionDataset", sessionDataset, envir = assign.env) sessionDataset$Score[1] <- sessionDataset$Score[1] + 4 assign("sessionDataset", sessionDataset, envir = assign.env) sessionDataset$Interval[1] <- as.difftime(1, units = "days") #+1 day assign("sessionDataset", sessionDataset, envir = assign.env) dueDate_new <- Sys.Date() + sessionDataset$Interval[1] assign("dueDate_new", dueDate_new, envir = assign.env) sessionDataset$dueDate[1] <- as.character.Date(dueDate_new) assign("sessionDataset", sessionDataset, envir = assign.env) write.csv(sessionDataset, file = paste0("", datasetAbsolutePath, ""), row.names = FALSE) } else if (sessionDataset$Repetition[1] == 1) { sessionDataset$Repetition[1] <- sessionDataset$Repetition[1] + 1 assign("sessionDataset", sessionDataset, envir = assign.env) sessionDataset$Score[1] <- sessionDataset$Score[1] + 4 assign("sessionDataset", sessionDataset, envir = assign.env) sessionDataset$Interval[1] <- as.difftime(4, units = "days") #+4 days (like Anki) assign("sessionDataset", sessionDataset, envir = assign.env) dueDate_new <- Sys.Date() + sessionDataset$Interval[1] assign("dueDate_new", dueDate_new, envir = assign.env) sessionDataset$dueDate[1] <- as.character.Date(dueDate_new) assign("sessionDataset", sessionDataset, envir = assign.env) write.csv(sessionDataset, file = paste0("", datasetAbsolutePath, ""), row.names = FALSE) } else if (sessionDataset$Repetition[1] > 1) { sessionDataset$Repetition[1] <- sessionDataset$Repetition[1] + 1 assign("sessionDataset", sessionDataset, envir = assign.env) sessionDataset$Score[1] <- sessionDataset$Score[1] + 4 # SuperMemo 2 algorithm below: sessionDataset$eFactor[1] <- max(1.3, sessionDataset$eFactor[[1]]+(0.1-(5-5)*(0.08+(5-5)*0.02))) assign("sessionDataset", sessionDataset, envir = assign.env) sessionDataset$Interval[1] <- (sessionDataset$Interval[[1]] - 1)*sessionDataset$eFactor[[1]] assign("sessionDataset", sessionDataset, envir = assign.env) dueDate_new <- Sys.Date() + sessionDataset$Interval[[1]] assign("dueDate_new", dueDate_new, envir = assign.env) sessionDataset$dueDate[1] <- as.character.Date(dueDate_new) assign("sessionDataset", sessionDataset, envir = assign.env) write.csv(sessionDataset, file = paste0("", datasetAbsolutePath, ""), row.names = FALSE) }) # reorder dataset by dueDate and Score sessionDataset <- sessionDataset[order(sessionDataset$dueDate, sessionDataset$Score), ] assign("sessionDataset", sessionDataset, envir = assign.env) write.csv(sessionDataset, file = paste0("", datasetAbsolutePath, ""), row.names = FALSE) invisible() return(sessionQuestions()) # create loop }

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\name{plot.mcmckingui} \alias{plot.mcmckingui} %- Also NEED an '\alias' for EACH other topic documented here. \title{ %% ~~function to do ... ~~ S3 method to plot for calss 'mcmckingui' } \description{ %% ~~ A concise (1-5 lines) description of what the function does. ~~ } \usage{ plot.mcmckingui(object, fname1, fname2, pch = 1, device = "wmf", ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{object}{ %% ~~Describe \code{object} here~~ An object of class 'mcmckingui' } \item{fname1}{ %% ~~Describe \code{fname1} here~~ The file name of the density plot. } \item{fname2}{ %% ~~Describe \code{fname2} here~~ The file name of the correlation plot. } \item{pch}{ %% ~~Describe \code{pch} here~~ What kind of points to use in the plots. } \item{device}{ %% ~~Describe \code{device} here~~ The plot device to be used. } \item{\dots}{ %% ~~Describe \code{\dots} here~~ Other arguments to be passed to 'plot'. } } \details{ %% ~~ If necessary, more details than the description above ~~ } \value{ Density and Correlation plots of the sampled parameters in 'wmf' or other format. %% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ... } \references{ %% ~put references to the literature/web site here ~ } \author{ %% ~~who you are~~ Zhenglei Gao } \note{ %% ~~further notes~~ } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ %% ~~objects to See Also as \code{\link{help}}, ~~~ } \examples{ \dontrun{ } } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{ Summary Statistics and Plots }

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bvi_plot <- function(bvi_scores){ library(ggplot2) library(dplyr) library(tidyr) taxunits <- colnames(bvi_scores)[1] bvi_scores %>% select(-c(BVI, rBVI)) %>% gather(Sample, Score, -1) %>% set_colnames(value = c("Spp", "Sample", "Score")) %>% ggplot(aes(x = Sample, y = Score, fill = Spp)) + geom_col(position = "fill", color = "black") + theme_bw() }

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library(HMM) states <- c('M1', 'M2', 'M3', 'M4', 'M5', 'I1', 'I2', 'I3', 'I4') symbols <- c('A', 'T', 'C', 'G') t1 <- 0.9 t2 <- 0.1 t3 <- 0.4 t4 <- 0.6 t5 <- 0 t6 <- 0 transitions <- matrix(data=c(0, t1, 0, 0, 0, t2, 0, 0, 0, 0, 0, t1, 0, 0, 0, t2, 0, 0, 0, 0, 0, t1, 0, 0, 0, t2, 0, 0, 0, 0, t5, t1, 0, 0, 0, t2, 0, 0, 0, 0, t6, 0, 0, 0, 0, 0, t4, 0, 0, 0, t3, 0, 0, 0, 0, 0, t4, 0, 0, 0, t3, 0, 0, 0, 0, 0, t4, 0, 0, 0, t3, 0, 0, 0, 0, 0, t4, 0, 0, 0, t3), byrow=TRUE, nrow=9, dimnames=list(states, states)) ei <- 0.25 emissions <- matrix(data=c(0.7, 0.1, 0.1, 0.1, 0.1, 0.1, 0.7, 0.1, 0.1, 0.8, 0.1, 0.0, 0.1, 0.1, 0.1, 0.7, 0.8, 0.0, 0.0, 0.2, ei, ei, ei, ei, ei, ei, ei, ei, ei, ei, ei, ei, ei, ei, ei, ei), byrow=TRUE, nrow=9, dimnames=list(states, symbols)) startProbs <- c(0.2, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0) hmm = initHMM(states, symbols, startProbs=startProbs, transProbs=transitions, emissionProbs=emissions) observations <- c('A', 'C', 'T', 'G', 'A') print(viterbi(hmm, observations)) observations <- c('A', 'G', 'C', 'T', 'G', 'A') print(viterbi(hmm, observations)) observations <- c('A', 'G', 'C', 'C', 'T', 'G', 'A') print(viterbi(hmm, observations)) fundamental <- transitions[-5,-5] fundamental <- solve(diag(length(fundamental[,1])) - fundamental) print(fundamental) avg_len <- 0 for (i in 1:(length(fundamental[,1])/2)) { print(i) avg_len <- avg_len + sum(fundamental[i,]) } avg_len <- avg_len / (length(fundamental[,1])/2) print(avg_len) observations<-c("T", "A", "A", "A", "C", "T", "G", "A", "T", "T", "T") print(viterbi(hmm,observations))

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ec2_operations.R \name{ec2_describe_network_insights_access_scope_analyses} \alias{ec2_describe_network_insights_access_scope_analyses} \title{Describes the specified Network Access Scope analyses} \usage{ ec2_describe_network_insights_access_scope_analyses( NetworkInsightsAccessScopeAnalysisIds = NULL, NetworkInsightsAccessScopeId = NULL, AnalysisStartTimeBegin = NULL, AnalysisStartTimeEnd = NULL, Filters = NULL, MaxResults = NULL, DryRun = NULL, NextToken = NULL ) } \arguments{ \item{NetworkInsightsAccessScopeAnalysisIds}{The IDs of the Network Access Scope analyses.} \item{NetworkInsightsAccessScopeId}{The ID of the Network Access Scope.} \item{AnalysisStartTimeBegin}{Filters the results based on the start time. The analysis must have started on or after this time.} \item{AnalysisStartTimeEnd}{Filters the results based on the start time. The analysis must have started on or before this time.} \item{Filters}{There are no supported filters.} \item{MaxResults}{The maximum number of results to return with a single call. To retrieve the remaining results, make another call with the returned \code{nextToken} value.} \item{DryRun}{Checks whether you have the required permissions for the action, without actually making the request, and provides an error response. If you have the required permissions, the error response is \code{DryRunOperation}. Otherwise, it is \code{UnauthorizedOperation}.} \item{NextToken}{The token for the next page of results.} } \description{ Describes the specified Network Access Scope analyses. See \url{https://www.paws-r-sdk.com/docs/ec2_describe_network_insights_access_scope_analyses/} for full documentation. } \keyword{internal}

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no_license

komalsrathi/MendelianRNA-seq-analysis

ed77e3990efb59d32e3f2b756827ec26d605ce29

301b0e01bea6e0b9db810268c0331992e3385c4e

refs/heads/master

2020-07-03T07:00:02.523330

2020-01-20T20:35:51

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7,048

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filter_variants.R

#################################################################### # Author: Komal S Rathi # Date: 01/31/2019 # Function: script to filter variants from GATK, Vardict and Strelka # Mahdi's pipeline filters # 1. ROI filter: exons +/- 10 # 2. Qual by depth: 5 for low GQ/low DP variants # 3. Population Filters: # HGMD variants 1%, synonymous variants 0.1%, other variants 0.5% # how many remain - save to another file # Step 1 (after annotating maf with HGMD) #################################################################### library(data.table) library(hutils) library(tidyr) library(GenomicRanges) library(reshape2) library(dplyr) # setwd('/mnt/isilon/cbmi/variome/rathik/mendelian_rnaseq') setwd('~/Projects/DGD_Mendelian_RNASeq/') # use exon file from biomart output exon.dat <- read.delim('data/gencode.v19.cdl_canonical_transcripts.v7.patched_contigs.exons.txt', stringsAsFactors = F) exon.dat <- exon.dat %>% group_by(gene_symbol) %>% mutate(exon_start = exon_start - 10, exon_end = exon_end + 10) %>% unique() %>% dplyr::select(gene_symbol, chrom, exon_start, exon_end) %>% as.data.frame() # final exons/intron list # introns <- read.delim('data/variant_filtering/final_splicevariants_exons.txt', stringsAsFactors = F) # introns <- unique(introns[,c("Sample","HGVSc","Introns","Label","Hugo_Symbol")]) # vars to test (for testing) # vars.to.test <- c("c.64","c.359","c.4561") # vars.to.test <- paste(vars.to.test, collapse = "|") # folder folder <- 'data/variant_filtering/rawdata/gatk3-source/' filter.out <- function(folder){ lf <- list.files(path = folder, pattern = '*.maf', full.names = TRUE) for(i in 1:length(lf)){ print(paste0("Sample no.: ",i)) n <- gsub('.*/|-hgmdannotated.maf|-gatk-haplotype-annotated-hgmdannotated.maf|.variants-hgmdannotated.maf|.vardict-annotated-rnaedit-annotated-gemini-hgmdannotated.maf|Sample_1__','',lf[i]) print(paste0("Sample: ",n)) dat <- data.table::fread(lf[i], verbose = FALSE) dat <- as.data.frame(dat) dat <- dat[which(dat$variant_qual != "."),] dat$Tumor_Sample_Barcode <- n # add Tumor Sample Barcode dat$var_id <- paste0(dat$Tumor_Sample_Barcode,'_', rownames(dat)) # ROI filter: # identify all exonic variants # add sequential identifiers exonic.vars <- dat exonic.vars$id <- seq(1:nrow(exonic.vars)) # only keep positions that are within the exons in the exon file subject <- with(exon.dat, GRanges(chrom, IRanges(start = exon_start, end = exon_end, names = gene_symbol))) query <- with(exonic.vars, GRanges(Chromosome, IRanges(start = Start_Position, end = End_Position, names = id))) # find overlaps and subset maf res <- findOverlaps(query = query, subject = subject, type = "within") res.df <- data.frame(exonic.vars[queryHits(res),], exon.dat[subjectHits(res),]) exonic.vars <- exonic.vars[which(exonic.vars$id %in% res.df$id),] exonic.vars$id <- NULL print(paste0("Dimensions of exonic vars: ", nrow(exonic.vars))) dat <- exonic.vars maf <- dat # this is for writing out full maf maf[which(maf$var_id %in% dat$var_id),"F1"] <- "Y" s0 <- nrow(dat) print(s0) # Quality Filters: # quality by depth >= 2 dat$variant_qual <- as.numeric(dat$variant_qual) dat <- dat[which(dat$variant_qual >= 30),] maf[which(maf$var_id %in% dat$var_id),"F2"] <- "Y" s1 <- nrow(dat) print(s1) # Population Filters # if HGMD annotated, AF <= 0.01 else Syn variants <= 0.001 and others <= 0.005 # replace gnomAD NAs with 0s dat[,grep('gnomAD_.*AF$', colnames(dat))][is.na(dat[,grep('gnomAD_.*AF$', colnames(dat))])] <- 0 dat$AF_filter <- FALSE dat$gnomAD_max_AF <- apply(dat[,grep('gnomAD_[A-Z]{3}_AF',colnames(dat))], 1, max) print(summary(dat$gnomAD_max_AF)) dat$AF_filter <- ifelse(is.na(dat$CLASS), ifelse(dat$gnomAD_max_AF <= 0.001, TRUE, FALSE), ifelse(dat$gnomAD_max_AF <= 0.005, TRUE, FALSE)) dat <- dat[which(dat$AF_filter == TRUE),] maf[which(maf$var_id %in% dat$var_id),"F3"] <- "Y" s2 <- nrow(dat) print(s2) # add sample name to tumor_sample_barcode t <- data.frame(sample = n, exonic = nrow(exonic.vars), F1 = s0, F2 = s1, F3 = s2) if(i == 1){ total1 <- t total2 <- dat total3 <- maf } else { total1 <- rbind(total1, t) total2 <- rbind(total2, dat) total3 <- rbind(total3, maf) } } # return results return(list(total1, total2, total3)) } # exonic pipeline vardict.total <- filter.out(folder = 'data/variant_filtering/rawdata/vardict/') write.table(vardict.total[[1]], file = 'data/variant_filtering/vardict_filtered_variants.txt', quote = F, sep = "\t", row.names = F) write.table(vardict.total[[2]], file = 'data/variant_filtering/vardict_filtered_variants.maf', quote = F, sep = "\t", row.names = F) write.table(vardict.total[[3]], file = 'data/variant_filtering/vardict_filter_breakdown_variants.maf', quote = F, sep = "\t", row.names = F) gatk4.total <- filter.out(folder = 'data/variant_filtering/rawdata/gatk4/') write.table(gatk4.total[[1]], file = 'data/variant_filtering/gatk4_filtered_variants.txt', quote = F, sep = "\t", row.names = F) write.table(gatk4.total[[2]], file = 'data/variant_filtering/gatk4_filtered_variants.maf', quote = F, sep = "\t", row.names = F) write.table(gatk4.total[[3]], file = 'data/variant_filtering/gatk4_filter_breakdown_variants.maf', quote = F, sep = "\t", row.names = F) strelka.total <- filter.out(folder = 'data/variant_filtering/rawdata/strelka/') write.table(strelka.total[[1]], file = 'data/variant_filtering/strelka_filtered_variants.txt', quote = F, sep = "\t", row.names = F) write.table(strelka.total[[2]], file = 'data/variant_filtering/strelka_filtered_variants.maf', quote = F, sep = "\t", row.names = F) write.table(strelka.total[[3]], file = 'data/variant_filtering/strelka_filter_breakdown_variants.maf', quote = F, sep = "\t", row.names = F) # gatk 3.8 (bcbio) gatk3.total <- filter.out(folder = 'data/variant_filtering/rawdata/gatk3/') write.table(gatk3.total[[1]], file = 'data/variant_filtering/gatk3_filtered_variants.txt', quote = F, sep = "\t", row.names = F) write.table(gatk3.total[[2]], file = 'data/variant_filtering/gatk3_filtered_variants.maf', quote = F, sep = "\t", row.names = F) write.table(gatk3.total[[3]], file = 'data/variant_filtering/gatk3_filter_breakdown_variants.maf', quote = F, sep = "\t", row.names = F) # gatk 3.8 (source) gatk3.total <- filter.out(folder = 'data/variant_filtering/rawdata/gatk3-source/') write.table(gatk3.total[[1]], file = 'data/variant_filtering/gatk3_source_filtered_variants.txt', quote = F, sep = "\t", row.names = F) write.table(gatk3.total[[2]], file = 'data/variant_filtering/gatk3_source_filtered_variants.maf', quote = F, sep = "\t", row.names = F) write.table(gatk3.total[[3]], file = 'data/variant_filtering/gatk3_source_filter_breakdown_variants.maf', quote = F, sep = "\t", row.names = F)

435071f7d57ee97139b3bdad7f3ed5a814ecbdd8

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/R/parse_taxolist.R

ff6703f72883dae200c964ec3c3312e870dbcc87

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XingXiong/bioparser

db44cf1ec5e3e49cb73af3cd93b4bffd209f7b23

1f11a018563fe5c7ad737c4e3d01dcb0f220c5a7

refs/heads/master

2021-01-23T13:30:47.438492

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parse_taxolist.R

#' Parse and extract taxonomic names from txt files #' #' \code{parse_taxolist} reads and parses all text lines from a file which contains #' taxonomic names, authors and distribution in each row and writes the tabular output #' to a csv file automatically or based on the configuration \code {config} specified #' by the users. #' #' @import RCurl #' @import plyr #' @import stringr #' #' @param input_file Required. The path and name of the file which the data is to be #' read from. If it does not contain an absolute path, the file name is relative to the #' current working directory. #' @param output_file Required. The path and name of the file for writing. If it does #' not contain an absolute path, the file name is relative to the current working #' directory. #' @param location_detail Optional.A logical value indicating whether the detailed #' information including longitude, latitude and detail location names of distributions #' is to be exported. Defaults to TRUE. #' @param language Optional.The language of detailed distribution information which #' must be one of "English" or "Chinese". Defaults to "English". #' @param evaluation Optional. A logical value indicating whether the evaluation of the #' parsing result is to be exported. Defaults to TRUE. #' @param config Optional. If it is not specified by users, the output will be generated #' automatically based on the structure of input texts. If it is indicated explicitly by #' users, the function will parse the input texts based on the rules specified in the #' \code{config}. Some examples of config are provided in the "Examples" part. Note that: #' Author_year should be regarded as a whole part; The separator between author_year part #' and distribution part should be stated clearly; If '\n' exsits, it can only appear #' right after the \code{genus} part. #' #' @return A data frame containing the result of parsing taxonomic names in the input #' file and detailed distribution information about species. For those taxonomic names #' which have more than one distribution, if \code{location_detail} is \code{TRUE}, each #' row in the data frame will only contain one distribution. If \code{location_detail} #' is \code{FALSE}, all distributions for a single species will be written in one row. #' #' A CSV file written from the above data frame. #' #' @examples \dontrun{ #' # example1: #' parse_taxolist(input_file = "./Examples/input_data/test_example.txt", #' output_file = "./Examples/output_data/test_example_output.csv", #' location_detail = TRUE, #' language = "English", #' evaluation = TRUE, #' config = "") #' #' input example: #' Charmus indicus Chiang, Chen & Zhang 1996.Distribution: Andhra Pradesh, Kerala,Pondicherry and Tamil Nadu. #' Isometrus maculatus De Geer, 1778.Distribution: Kerala, Andhra Pradesh, Madhya Pradesh, Karnataka,Maharashtra, Meghalaya and Tamil Nadu. #' Lychas hendersoni Pocock, 1897) Distribution: Kerala and Tamil Nadu. #' #' #' # example2: #' parse_taxolist(input_file = "./Examples/input_data/test_example_config_1.txt", #' output_file = "./Examples/output_data/test_example_output_config_1.csv", #' location_detail = FALSE, #' language = "English", #' evaluation = TRUE, #' config = "Genus, Species, Author_Year, 'Distribution:', distribution") #' #' input example: #' Pachliopta pandiyana Moore, 1881 Distribution: Goa, Karnataka, Kerala #' #' #' # example3: #' parse_taxolist(input_file = "./Examples/input_data/test_example_config_2.txt", #' output_file = "./Examples/output_data/test_example_output_config_2.csv", #' location_detail = FALSE, #' language = "English", #' evaluation = FALSE, #' config = "Genus, Species, Author_Year, ':', distribution") #' #' input example: #' Pachliopta pandiyana Moore, 1881 : Goa, Karnataka, Kerala #' #' #' # example4: #' parse_taxolist(input_file = "./Examples/input_data/test_example_config_3.txt", #' output_file = "./Examples/output_data/test_example_output_config_3.csv", #' location_detail = TRUE, #' language ="English", #' evaluation = FALSE, #' config = "Genus, '\n', Species, Author_Year, ':',distribution") #' #' input example: #' Pachliopta #' pandiyana Moore, 1881 : Goa, Karnataka, Kerala #' aristolochiae Fabricius, 1775 : Meghalaya, Paschimbanga, Kerala, Karnataka, Arunachal Pradesh, Telangana, Andhra Pradesh, Maharashtra, Gujarat, Odisha, Chhattisgarh #'} #' #' @export parse_taxolist <- function(input_file, output_file, location_detail, language, evaluation, config){ lines <- readLines(input_file, warn = FALSE) final_result <- c() if(config == ""){ for (i in 1:length(lines)){ if (lines[i] != ""){ cur_sci_name <- get_full_sciname_one_line(lines[i]) cur_result <- get_tabular_output(cur_sci_name, location_detail, language, evaluation) final_result <- rbind(final_result, cur_result) } } write.csv(final_result, file = output_file, row.names = F) } else { config_list <- as.list(strsplit(config, ",")[[1]]) config_list <- str_trim(config_list) line_break_num <- length(str_locate_all(config, "\n")[[1]][,"start"]) if(line_break_num == 0){ for (i in 1:length(lines)){ if (lines[i] != ""){ cur_sci_name <- get_sciname_by_config_one_line(lines[i], config) cur_result <- get_tabular_output(cur_sci_name, location_detail, language, evaluation) final_result <- rbind(final_result, cur_result) } } write.csv(final_result, file = output_file, row.names = F) } else if(line_break_num == 1){ output_sciname_by_config_multi_line(config, lines, output_file, location_detail, language, evaluation) } else{ Stop("You can't have more than one line break symbols in config.") } } return(final_result) } getGenus <- function(science_name){ # if the first character is capitalized, then the first word must be Genus # quality: score to evaluate results (1: good, 2: uncertainty in some part, 3: possibly error in input) if (str_detect(science_name, "^[A-Z]")){ capital_index <- str_locate_all(science_name,"[[:blank:]]?[A-Z]+") genus_species_sub <- str_sub(science_name, capital_index[[1]][1], capital_index[[1]][2]-1) split <- str_split(genus_species_sub, " ") split <- unlist(split) if (length(split) == 1){ # if there's only one word genus <- split[1] species <- 'NA' subspecies <- 'NA' score <- 1 evaluation <- "High credibility." } else if (length(split) == 2){ # if there are two words genus <- split[1] species <- split[2] subspecies <- 'NA' score <- 1 evaluation <- "High credibility." } else if (length(split) == 3){ # if there are three words genus <- split[1] species <- split[2] subspecies <- split[3] score <- 1 evaluation <- "High credibility." } else { # if there are more than three words in the fist part genus <- split[1] species <- split[2] subspecies <- split[3] score <- 3 evaluation <- "Possibly error or confusion in the input." } # if the first character is not capitalized, then this entry doesn't include Genus } else { capital_index <- str_locate_all(science_name,"[[:blank:]]?[A-Z]+") genus_species_sub <- str_sub(science_name, 1, capital_index[[1]][1]-1) split <- str_split(genus_species_sub, " ") split <- unlist(split) if (length(split) == 1){ # to be continued (how to distinguish between species and subspecies? ) genus <- 'NA' species <- split[1] subspecies <- 'NA' score <- 2 evaluation <- "Cannot distinguish species or subspecies." } else if (length(split) == 2) { genus <- 'NA' species <- split[1] subspecies <- split[2] score <- 1 evaluation <- "High credibility." } else { genus <- 'NA' species <- split[1] subspecies <- split[2] score <- 3 evaluation <- "Possibly error or confusion in the input." } } return (list(genus, species, subspecies, score, evaluation)) } getAuthorYear <- function(science_name){ if(str_detect(science_name, "[0-9]{4}.?")){ year_list <- str_locate_all(science_name, "[0-9]{4}.?")[[1]] # end_index is the position of the last character of the last year end_index <- year_list[, "end"][length(year_list[, "end"])] capital_index <- str_locate_all(science_name,"[[:blank:]]?[A-Z]+") if(str_detect(science_name, "^[A-Z]")){ # if scientific name starts with a capital letter (genus), then the second capital letter is the start of author_year part start_index <- capital_index[[1]][2] } else { # if not, then the first capital letter is the start of author_year part start_index <- capital_index[[1]][1] } author_year_list <- str_sub(science_name, start_index + 1, end_index - 1) score <- 1 evaluation <- "High credibility." } else { author_year_list <- 'NA' score <- 1 evaluation <- "There's no author info in this entry." } return(c(author_year_list, score, evaluation)) } getDistribution <- function(science_name){ # if there is year part in the scientific name, the distribution info is right after the last year. if(str_detect(science_name, "[0-9]{4}.?")){ year_list <- str_locate_all(science_name, "[0-9]{4}.?")[[1]] # start_index is the position of the last character of the last year start_index <- year_list[, "end"][length(year_list[, "end"])] # if there's no author_year part, the distribution info starts with the second (or first) capital letter in the scientific name } else { capital_index <- str_locate_all(science_name,"[[:blank:]]?[A-Z]+") if(str_detect(science_name, "^[A-Z]")){ start_index <- capital_index[[1]][2] } else{ start_index <- capital_index[[1]][1] } } distribution_list <- substring(science_name, start_index + 1) clean_list <- gsub("\\sand\\s", ",", distribution_list) clean_list <- gsub("Distribution:", "", clean_list) clean_list <- str_trim(gsub("[:punct:]", "", clean_list)) if(str_detect(science_name, ",") == FALSE & length(str_split(clean_list, " ")[[1]]) != 1){ distribution <- str_split(clean_list, " ") } else { distribution <- str_split(clean_list, ",") } score <- 1 evaluation <- "High credibility." # if there's no distribution info, to be continued return(c(distribution, score, evaluation)) } # connect with google map api to get the longitude, altitude and detailed information about the distribution url <- function(address, language, return.call = "json", sensor = "false") { if (language == "English"){ root <- "http://maps.google.com/maps/api/geocode/" u <- paste(root, return.call, "?address=", address, "&sensor=", sensor, sep = "") return(URLencode(u)) } else { root <- "http://maps.google.cn/maps/api/geocode/" u <- paste(root, return.call, "?address=", address, "&sensor=", sensor, sep = "") return(URLencode(u)) } } geoCode <- function(address, language, verbose = FALSE) { if(verbose) cat(address,"\n") u <- url(address, language) doc <- getURL(u) x <- fromJSON(doc, simplify = FALSE) if(x$status == "OK") { lat <- x$results[[1]]$geometry$location$lat lng <- x$results[[1]]$geometry$location$lng location_type <- x$results[[1]]$geometry$location_type formatted_address <- x$results[[1]]$formatted_address return(c(lat, lng, location_type, formatted_address)) } else { return(c(NA, NA, NA, NA)) } } evaluation_output <- function(sci_name){ score <- max(sci_name$genus_score,sci_name$author_year_score,sci_name$distribution_score) evaluation <- paste("genus:", sci_name$genus_evaluation, "author:", sci_name$author_year_evaluation, "distribution:", sci_name$distribution_evaluation, sep = " ") return(c(score,evaluation)) } get_full_sciname_one_line <- function(line){ sci_name <-c() sci_name$genus <- 'NA' sci_name$species <- 'NA' sci_name$subspecies <- 'NA' sci_name$author_year <- 'NA' sci_name$distribution <- 'NA' sci_name$genus_score <- -1 sci_name$genus_evaluation <- '' sci_name$author_year_score <- -1 sci_name$author_year_evaluation <- '' sci_name$distribution_score <- -1 sci_name$distribution_evaluation <- '' # get genus, species, subspecies and its evaluation genus_species_sub <- getGenus(line) sci_name$genus <- unlist(genus_species_sub)[1] sci_name$species <- unlist(genus_species_sub)[2] sci_name$subspecies <- unlist(genus_species_sub)[3] sci_name$genus_score <- unlist(genus_species_sub)[4] sci_name$genus_evaluation <- unlist(genus_species_sub)[5] # get author year info and the evaluation author_year_result <- getAuthorYear(line) sci_name$author_year <- unlist(author_year_result)[1] sci_name$author_year_score <- unlist(author_year_result)[2] sci_name$author_year_evaluation <- unlist(author_year_result)[3] # get basic distribution distribution_result <- getDistribution(line) distribution_list <- distribution_result[1] sci_name$distribution_score <- distribution_result[2][[1]] sci_name$distribution_evaluation <- distribution_result[3][[1]] sci_name$distribution <- paste(distribution_result[[1]], collapse = ",") return(sci_name) } get_sciname_by_config_one_line <- function(line, config){ sci_name <-c() sci_name$genus <- 'NA' sci_name$species <- 'NA' sci_name$subspecies <- 'NA' sci_name$author_year <- 'NA' sci_name$distribution <- 'NA' sci_name$genus_score <- -1 sci_name$genus_evaluation <- '' sci_name$author_year_score <- -1 sci_name$author_year_evaluation <- '' sci_name$distribution_score <- -1 sci_name$distribution_evaluation <- '' config_list <- as.list(strsplit(config, ",")[[1]]) config_list <- str_trim(config_list) len = length(config_list) if("Genus" %in% str_trim(config_list)){ genus_pos <- get_config_position(config_list, "Genus") sci_name$genus <- str_split(line, " ")[[1]][genus_pos] rest_part <- str_trim(gsub(sci_name$genus, "", line)) sci_name$genus_score <- 1 sci_name$genus_evaluation <- "High credibility." } else { sci_name$genus_score <- 1 sci_name$genus_evaluation <- "Missing 'genus' in the scientific name." } if("Species" %in% str_trim(config_list)){ species_pos <- get_config_position(config_list, "Species") sci_name$species <- str_split(line, " ")[[1]][species_pos] rest_part <- str_trim(gsub(sci_name$species, "", rest_part)) } else { if (sci_name$genus_evaluation == "High credibility."){ sci_name$genus_score <- 1 sci_name$genus_evaluation <- "Missing 'species' in the scientific name." } else { sci_name$genus_score <- 1 sci_name$genus_evaluation <- paste(sci_name$genus_evaluation, "Missing 'species' in the scientific name.") } } if("Subspecies" %in% str_trim(config_list)){ subspecies_pos <- get_config_position(config_list, "Subspecies") sci_name$subspecies <- str_split(line, " ")[[1]][subspecies_pos] rest_part <- str_trim(gsub(sci_name$subspecies, "", rest_part)) } else { if (sci_name$genus_evaluation == "High credibility."){ sci_name$genus_score <- 1 sci_name$genus_evaluation <- "Missing 'subspecies' in the scientific name." } else { sci_name$genus_score <- 1 sci_name$genus_evaluation <- paste(sci_name$genus_evaluation, "Missing 'subspecies' in the scientific name.") } } # if config list ends with distribution if(str_trim(config_list[len]) == 'distribution'){ # extract distribution part if (str_detect(config_list[len-1], "'*'")){ split_word <- str_split(config_list[len-1], "'*'")[[1]][2] sci_name$distribution <- str_trim(str_split(rest_part, split_word)[[1]][2]) rest_part <- str_trim(gsub(split_word, "", rest_part)) sci_name$author_year <- str_trim(gsub(sci_name$distribution , "", rest_part)) sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "High credibility." if (sci_name$author_year != ""){ sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "High credibility." } else { sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "Missing author and year part in the scientific name." } } # if config list ends with author and year part else if (str_trim(config_list[len-1]) == 'Author_Year'){ year_list <- str_locate_all(rest_part, "[0-9]{4}.?")[[1]] # start_index is the position of the last character of the last year start_index <- year_list[, "end"][length(year_list[, "end"])] sci_name$distribution <- str_trim(substring(rest_part, start_index + 1)) sci_name$author_year <- str_trim(gsub(sci_name$distribution, "", rest_part)) sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "High credibility." sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "Missing distribution part in the scientific name." } # if config list does not contain author_year and distribution else { sci_name$distribution <- rest_part sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "High credibility." sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "Missing author and year part in the scientific name." } } else if (str_trim(config_list[len]) == 'Author_Year'){ sci_name$author_year <- rest_part sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "Missing distribution part in the scientific name." } else{ sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "Missing author and year part in the scientific name." sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "Missing distribution part in the scientific name." } return(sci_name) } output_sciname_by_config_multi_line <- function(config, lines, output_file, location_detail, language, evaluation){ sci_name <-c() sci_name$genus <- 'NA' sci_name$species <- 'NA' sci_name$subspecies <- 'NA' sci_name$author_year <- 'NA' sci_name$distribution <- 'NA' sci_name$genus_score <- -1 sci_name$genus_evaluation <- '' sci_name$author_year_score <- -1 sci_name$author_year_evaluation <- '' sci_name$distribution_score <- -1 sci_name$distribution_evaluation <- '' final_result <- c() config_list <- as.list(strsplit(config, ",")[[1]]) config_list <- str_trim(config_list) line_break_pos <- get_config_position(config_list, "'\n'") line_break_num <- length(str_locate_all(config, "\n")[[1]][,"start"]) if(line_break_num == 1 & line_break_pos ==2){ len <- length(lines) for(i in 1:len){ if(lines[i] == ""){ continue } line_split <- str_split(str_trim(lines[i]), " ")[[1]] if(length(line_split) == 1){ cur_line_index <- i sci_name$genus <- str_trim(lines[i]) } else { cur_config_list <- config_list[-1] cur_config_list <- cur_config_list[-1] cur_config_len <- length(cur_config_list) if("Species" %in% str_trim(cur_config_list)){ species_pos <- get_config_position(cur_config_list, "Species") sci_name$species <- str_split(lines[i], " ")[[1]][species_pos] lines[i] <- str_trim(gsub(sci_name$species, "", lines[i])) } else { sci_name$genus_score <- 1 sci_name$genus_evaluation <- "Missing 'species' in the scientific name." } if("Subspecies" %in% str_trim(cur_config_list)){ subspecies_pos <- get_config_position(cur_config_list, "Subspecies") sci_name$subspecies <- str_split(lines[i], " ")[[1]][subspecies_pos] lines[i] <- str_trim(gsub(sci_name$subspecies, "", lines[i])) } else { if (sci_name$genus_evaluation == "High credibility."){ sci_name$genus_score <- 1 sci_name$genus_evaluation <- "Missing 'subspecies' in the scientific name." } else { sci_name$genus_score <- 1 sci_name$genus_evaluation <- paste(sci_name$genus_evaluation, "Missing 'subspecies' in the scientific name.") } } if(str_trim(cur_config_list[cur_config_len]) == 'distribution'){ # extract distribution part if (str_detect(cur_config_list[cur_config_len-1], "'*'")){ split_word <- str_split(cur_config_list[cur_config_len-1], "'*'")[[1]][2] sci_name$distribution <- str_trim(str_split(lines[i], split_word)[[1]][2]) lines[i] <- str_trim(gsub(split_word, "", lines[i])) sci_name$author_year <- str_trim(gsub(sci_name$distribution , "", lines[i])) sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "High credibility." if (sci_name$author_year != ""){ sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "High credibility." } else { sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "Missing author and year part in the scientific name." } } else if (str_trim(cur_config_list[cur_config_len-1]) == 'Author_Year'){ year_list <- str_locate_all(lines[i], "[0-9]{4}.?")[[1]] # start_index is the position of the last character of the last year start_index <- year_list[, "end"][length(year_list[, "end"])] sci_name$distribution <- str_trim(substring(lines[i], start_index + 1)) sci_name$author_year <- str_trim(gsub(sci_name$distribution, "", lines[i])) sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "High credibility." sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "Missing distribution part in the scientific name." } else { sci_name$distribution <- lines[i] sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "High credibility." sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "Missing author and year part in the scientific name." } } else if (str_trim(cur_config_list[cur_config_len]) == 'Author_Year'){ sci_name$author_year <- lines[i] sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "Missing distribution part in the scientific name." } else { sci_name$author_year_score <- 1 sci_name$author_year_evaluation <- "Missing author and year part in the scientific name." sci_name$distribution_score <- 1 sci_name$distribution_evaluation <- "Missing distribution part in the scientific name." } # write the current line to tabular output cur_result <- get_tabular_output(sci_name, location_detail, language, evaluation) final_result <- rbind(final_result, cur_result) } } write.csv(final_result, file = output_file, row.names = F) } else { stop("Can't support current config.") } } get_tabular_output <- function(sci_name, location_detail, language, evaluation){ # create a new dataframe result <- data.frame(genus = c(0), species = c(0), subspecies = c(0), author_year = c(0), distribution = c(0), latitude = c(0), longitude = c(0), detail = c(0)) eva_df <- data.frame(score = c(0), evaluation = c(0)) if (location_detail == "TRUE"){ distribution_list <- str_split(sci_name$distribution, ",")[[1]] for (j in 1:length(distribution_list)){ # To make the table clear,the table will show information about genus,author and etc. for only one time # for different locations from the same entry. if (j == 1) { distribution_list[j] <- gsub("[[:punct:]]", "", distribution_list[j]) address <- geoCode(distribution_list[j], language) new_row <- c(sci_name$genus, sci_name$species, sci_name$subspecies, sci_name$author_year, distribution_list[j], address[1], address[2], address[4]) result <- rbind(result, new_row) if (evaluation == "TRUE"){ eva_result <- evaluation_output(sci_name) eva_df <- rbind(eva_df, eva_result) } } else { address <-geoCode(distribution_list[j], language) new_row <- c(" ", " ", " ", " ", distribution_list[j], address[1], address[2], address[4]) result <- rbind(result, new_row) if (evaluation == "TRUE"){ eva_df <- rbind(eva_df,c(" "," "))} } } # The server of api will reject too frequent access,so set a stop after each loop. Sys.sleep(1) } else{ if (evaluation == "TRUE"){ new_row <- c(sci_name$genus, sci_name$species, sci_name$subspecies, sci_name$author_year, sci_name$distribution) result <- rbind(result[,1:5], new_row) eva_result <- evaluation_output(sci_name) eva_df <- rbind(eva_df, eva_result) } else { new_row <- c(sci_name$genus, sci_name$species, sci_name$subspecies, sci_name$author_year, sci_name$distribution) result <- rbind(result[,1:5], new_row)} } if (evaluation == "TRUE"){ result = cbind(result[-1,],eva_df[-1,]) } else { result = result[-1,] } return(result) } parse_taxoname <- function(input_str, location_detail, language, evaluation, config){ lines <- str_split(input_str, "\n")[[1]] if(config == ""){ cur_sci_name <- get_full_sciname_one_line(input_str) } else { config_list <- as.list(strsplit(config, ",")[[1]]) config_list <- str_trim(config_list) line_break_num <- length(str_locate_all(config, "\n")[[1]][,"start"]) if(line_break_num == 0){ cur_sci_name <- get_sciname_by_config_one_line(input_str, config) } else{ Stop("You can't have more than one line break symbols in config.") } } return(cur_sci_name) }

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testlist <- list(rates = c(NaN, 7.29112072938316e-304, -1.64816262214147e-307, -2.35343736497682e-185, 7036874417766.4, -2.35343736826454e-185, 7.17736025324585e-310, 7.32777351949015e-15, 9.14021444806306e-322, 1.00891829368495e-309, 2.67904643304077e+301, -1.26836459123889e-30, 9.37339630957792e-312, 1.09509791288755e-303, -5.66365833702221e+303, 5.43230922486616e-312, -3.9759940224262e-34, -1.26823100659151e-30, -1.26836459270829e-30, 2.39422219319154e-301, -4.93185008441161e-31, NaN, -5.79189576537157e-34, -9.21253817446353e-280, 1.23269447171475e-30, -1.26836459270829e-30, 5.5869437297374e-319, -6.76385503750878e-231, -2.97578981702996e-288, 7.29112203319188e-304, 8.74601785204247e-310, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ), thresholds = numeric(0), x = c(NaN, NaN, NaN, 9.70488469130173e-101, 9.70418706716128e-101, 9.70418706716128e-101, 9.70418706716128e-101, 9.70418706716128e-101, -5.82789329454073e+303, -5.82900681339507e+303, 1.08646184497373e-311, 8.97712454626148e-308, -2.11840698478091e-289, 1.24351972100265e-13, -3.72626152437281e+304, Inf, -7.40367110377773e-171, NaN, NaN, NaN, -8.59702596077467e-171, 5.7418150925011e+199, -2.97598959778408e-288, 4.0083522360489e-306, 2.34012289634757e-269, -1.26836459270829e-30, 3.94108708470682e-312, -5.96890832358674e+306, 2.81218450871091e-312, 5.85363771866079e+170, 8.62805110310557e-307, -4.25550648705951e+305, NaN, NaN, NaN, NaN, -1.36573625663878e-151, 5.74181509254692e+199, -2.11852534547344e-289, NaN, -5.96890832411666e+306, NaN, -2.30879999750655e-289, 8.74601785371863e-310, 8.5451750570825e+194, -1.404447759072e+306, NaN, 7.2911220195564e-304, -8.8144298991562e-280, -2.30331110774114e-156, -2.30331110816477e-156, -1.12583501772562e-305, -2.30331110816477e-156, -6.36358120662016e+305, 3.65365169506523e-306, 2.62480682658967e-301, -6.56793027847815e-287, -8.81442988493713e-280, -2.64494692448922e+154, 1.86165782692909e-130, -1.40946656124468e-52, -6.5692055167788e-287, 2.11057949781812e-309, -2.29827867994584e-185, 9.11389926506012e-306, 2.39701938834909e-94, -2.18056649082045e-289, NaN, 2.7271398649941e-312, 7.14190420369699e-304, 5.43226988934558e-312, 0, 0, 2.71615461243555e-312, 7.14190420369699e-304, 5.43226988934558e-312, -1.03066467131803e-228, 2.71615461306795e-312, -1.90877083252549e-287, 7.41606077195142e-310, -1.74162172578232e-248, 7.20047258077813e-310, 3.98264587882868e-317, 8.28904605845809e-317, 2.84809453888922e-306, 6.9881578015912e-310, NA, -2.30331110816477e-156, -2.30331110816477e-156, -2.30331110816272e-156, -5.5516993870748e+306, 4.65661286890991e-10, 0)) result <- do.call(grattan::IncomeTax,testlist) str(result)

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escanillans/eulerian_path_finding_algorithm

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# This function is the forward algorithm # Input: # n = number of states # seq = sequence # Output: log forward probabilities for corresponding sequence position and state forward <- function(n, seq, transitions, emissions) { # create an empty matrix # dimension = n+2 x length(seq)+1, # where n = number of states (add two for begin and end states) logProbMatrix = matrix(data = rep(0, (n+2)*(length(seq)+1)), nrow = (n+2), ncol = (length(seq)+1)) # Initialize (1,1) to 1 logProbMatrix[1,1] = 1 # Fill in logProbMatrix Top-Down, Left-Right for(j in 2:ncol(logProbMatrix)) { for(i in 2:nrow(logProbMatrix)) { # Compute probability of generating first j chars and ending in state i # 1. Find out how many states point to state i pointerInfo = findPointers(transitions, i) sum = 0 for(k in 1:pointerInfo$numPointers) { currSum = logProbMatrix[pointerInfo$pointers[k],j-1] * pointerInfo$transProb[k] sum = sum + currSum } # 2. find emissionProb for current state emitProb = emissionProb(emissions, i-1, seq[i-1]) # 3. compute log prob logProbMatrix[i,j] = log(emitProb*sum) } } return(logProbMatrix) } # This function returns necessary emission probability # Input: # emissions = table of emission probabilities # stateID = state ID # symbol = emission symbol # Output: emissionProb <- function(emissions, stateID, symbol) { return(emissions[emissions$statNum == stateID & emissions$emissionSymb == symbol,3]) } # For current state, # output the number of pointers to state, # the states that point to currState, # and the transition probabilities. findPointers <- function(transitions, currState) { return(list(numPointers = length(transitions[transitions$to == currState,1]), pointers = transitions[transitions$to == currState,1], transProb = transitions[transitions$to == currState,3])) }

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id_into_character <- function(id){ #id is an integer vector id_char <- c() #creating an empty vector for id in character form for (i in seq_along(id)){ if (id[i] < 10){ #if the integer has only 1 digit id_char <- c(id_char, paste("00", as.character(id[i]), sep = "")) } else if (id[i] < 100){ #if the integer has 2 digit id_char <- c(id_char, paste("0", as.character(id[i]), sep = "")) } else if (id[i] <= 332) { #if the integer has 3 digit id_char <- c(id_char, as.character(id[i])) } } return(id_char) } url_list <- function(id){ # id is an integer vector id <- id_into_character(id) #turning id into a character vector url <- c() #creating an empty vector for the urls for (i in seq_along(id)){ url <- c(url, paste("./specdata/", id[i], ".csv",sep = "")) } return(url) } complete <- function(directory, id){ nobs <- c() #creating an empty vector to store complete cases count for (i in seq_along(id)){ data <- read.csv(url_list(id)[i]) complete_cases <- !is.na(data$nitrate) & !is.na(data$sulfate) nobs <- c(nobs, sum(complete_cases)) } return(as.data.frame(cbind(id, nobs))) } df <- complete(specdata, 1:332) corr <- function(directory, threshold = 0){ above_threshold <- which(df$nobs > threshold) #location of data above threshold above_threshold <- id_into_character(above_threshold) correlation <- c() for (i in seq_along(above_threshold)){ #obtaining all the correlations data <- read.csv(paste("./specdata/", above_threshold[i], ".csv",sep = "")) complete_cases <- !is.na(data$nitrate) & !is.na(data$sulfate) correlation <- c(correlation, cor(data$sulfate[complete_cases], data$nitrate[complete_cases])) } #printing the correlations if (length(correlation) > 0){ return(correlation) } if (length(correlation) == 0){ return(as.numeric(correlation)) } }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/remlSupport.R \name{pcc} \alias{pcc} \title{REML convergence checks} \usage{ pcc(object, traces = NULL, tol = 0.01, silent = FALSE) } \arguments{ \item{object}{A list with at least one element named: \code{monitor} (see Details)} \item{traces}{Optionally, a matrix to substitute instead of the monitor element to \code{object}. Each row corresponds to a different variance component in the model and each column is a different iteration of the likelihood calculation (column 1 is the first iterate).} \item{tol}{The tolerance level for which to check against all of the changes in variance component parameter estimates} \item{silent}{Optional argument to silence the output of helpful (indicating default underlying behavior) messages} } \value{ Returns \code{TRUE} if all variance parameters change less than the value specified by \code{tol}, otherwise returns \code{FALSE}. Also see the \code{details} section for other circumstances when \code{FALSE} might be returned. } \description{ Mainly checks to ensure the variance components in a REML mixed model do not change between the last two iterations more than what is allowed by the tolerance value. See details for extra check on asreml-R models. } \details{ Object is intended to be an asreml-R model output. NOTE, The first 3 rows are ignored and thus should not be variance components from the model (e.g., they should be the loglikelihood or degrees of freedom, etc.). Also, the last column is ignored and should not be an iteration of the model (e.g., it indicates the constraint). The function also checks \code{object} to ensure that the output from the asreml-R model does not contain a log-likelihood value of exactly 0.00. An ASReml model can sometimes fail while still returning a \code{monitor} object and \code{TRUE} value in the \code{converge} element of the output. This function will return \code{FALSE} if this is the case. } \examples{ # Below is the last 3 iterations from the trace from an animal model of # tait1 of the warcolak dataset. # Re-create the output from a basic, univariate animal model in asreml-R tracein <- matrix(c(0.6387006, 1, 0.6383099, 1, 0.6383294, 1, 0.6383285, 1), nrow = 2, ncol = 4, byrow = FALSE) dimnames(tracein) <- list(c("ped(ID)!ped", "R!variance"), c(6, 7, 8, 9)) pcc(object = NULL, trace = tracein) } \author{ \email{matthewwolak@gmail.com} }

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prabasiva/cchaos

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refs/heads/master

2021-06-02T14:07:39.186551

2018-01-09T15:29:37

67,543,385

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AutoCorrelation.R

ac<-function() { #Program used to create AutoCorrelation Analysis for sample, SP500 & NASDAQ #Filename:AutoCorrelation.R # Praba Siva # praba@umich.edu # @prabasiva library(mFilter); library(latex2exp) setwd("/Users/sivasp1/Documents/2016/Personal/Praba/MATH599/program") fspcom=read.table('fspcom.dat') dat = (fspcom[,5]) mort=log(dat) year=fspcom[,2]+1/12*fspcom[,3] le=length(dat) x=mort[2:le] y=mort[1:le-1] diffxy=x-y #plot(diffxy,type='l') dur=1:length(year) lmr=lm(mort~dur) intercept=coef(lmr)[1] slope=coef(lmr)[2] dftrend=intercept+slope*dur dfcycle=mort-dftrend dfacf=acf(dfcycle,plot=FALSE,100); hpf=hpfilter(mort,freq=14400) layout(matrix(c(1,2,3,4), 4,1, byrow = TRUE)) color={'blue'} ac1=acf(hpf$cycle, ci.type = "ma",plot=FALSE,100) plot(year,mort,main='Log SP500 index', xlab='Year',ylab=TeX('log (SP500(t))'), type='l',cex.axis=1.1,cex.lab=1.1,lwd=3,col='red'); bc1=acf(diffxy,ci.type="ma",plot=FALSE,100) plot(ac1,main='Autocorrelation of log SP500 HP Cycles' ,xlab='Lag',ylab='AC(1)') lines(ac1$lag,ac1$acf,main='Autocorrelation of log SP500 HP Cycles', xlab='Lag',ylab='AC(1)',type='l', col='blue',cex.axis=1.1,cex.lab=1.1,lwd=3) plot(bc1,main='Autocorrelation of log SP500 FD ', xlab='Lag',ylab='AC(1)') lines(bc1$lag,bc1$acf,main='Autocorrelation of log SP500 FD', xlab='Lag',ylab='AC(1)',type='l',col='blue',lwd=3) plot(dfacf,main='Autocorrelation of log-linear SP500 ', xlab='Lag',ylab='AC(1)') lines(dfacf$lag,dfacf$acf,main='Autocorrelation of log-linear SP500 ', xlab='Lag',ylab='AC(1)',type='l', col='blue',lwd=3) layout(matrix(c(1,2), 2,1, byrow = TRUE)) plot(year,mort,main='Log SP500 index', xlab='Year',ylab=TeX('log (SP500(t))'), type='l',cex.axis=1.1,cex.lab=1.1,lwd=3,col='red'); lines(year,dftrend,main='Trend of Log SP500 index using Log-linear', xlab='Year',ylab=TeX('log-linear(SP500(t))'), type='l',cex.axis=1.1,cex.lab=1.1,lwd=3,col='blue'); legend("bottomright",c("Trend"),lty=c(1),lwd=c(2.5),col=c("blue")) plot(year,dfcycle,main='Cycle of Log SP500 index using Log-linear', xlab='Year',ylab=TeX('log-linear(SP500(t))'), type='l',cex.axis=1.1,cex.lab=1.1,lwd=3,col='green'); layout(matrix(c(1,2), 2,1, byrow = TRUE)) plot(year,mort,main='Log SP500 index', xlab='Year',ylab=TeX('log (SP500(t))'), type='l',cex.axis=1.1,cex.lab=1.1,lwd=3,col='red'); plot(year[1:length(diffxy)],diffxy, main='Cycle of Log SP500 index using Log-linear trend', xlab='Year',ylab=TeX('log-linear(SP500(t))'),type='l', cex.axis=1.1,cex.lab=1.1,lwd=3,col='green'); sta.sp500=list(mean(dfacf$acf),sd(dfacf$acf),var(dfacf$acf),corrlength(dfacf), mean(ac1$acf),sd(ac1$acf),var(ac1$acf),corrlength(ac1), mean(bc1$acf),sd(bc1$acf),var(bc1$acf),corrlength(bc1)); layout(matrix(c(1,2,3,4), 4,1, byrow = TRUE)) setwd("/Users/sivasp1/Documents/2016/Personal/Praba/MATH599/program") dat <- read.csv(file="nasdaq_ready.csv",head=TRUE,sep=",") year=dat[,1]+1/12*dat[,2] dat=dat[,3] mort=log(dat) le=length(dat) x=mort[2:le] y=mort[1:le-1] diffxy=x-y dur=1:length(year) lmr=lm(mort~dur) intercept=coef(lmr)[1] slope=coef(lmr)[2] dftrend=intercept+slope*dur dfcycle=mort-dftrend dfacf=acf(dfcycle,plot=FALSE,100); hpf=hpfilter(mort,freq=14400) ac1=acf(hpf$cycle, ci.type = "ma",plot=FALSE,100) bc1=acf(diffxy,ci.type="ma",plot=FALSE,100) layout(matrix(c(1,2), 2,1, byrow = TRUE)) plot(year,mort,main='Log NASDAQ index', xlab='Year',ylab=TeX('log (NASDAQ(t))'), type='l',cex.axis=1.1,cex.lab=1.1,lwd=3,col='red'); lines(year,dftrend,main='Trend of Log NASDAQ index using Log-linear', xlab='Year',ylab=TeX('log-linear(NASDAQ(t))'), type='l',cex.axis=1.1,cex.lab=1.1,lwd=3,col='blue'); legend("bottomright",c("Trend"),lty=c(1),lwd=c(2.5),col=c("blue")) plot(year,dfcycle,main='Cycle of Log NASDAQ index using Log-linear', xlab='Year',ylab=TeX('log-linear(NASDAQ(t))'), type='l',cex.axis=1.1,cex.lab=1.1,lwd=3,col='green'); layout(matrix(c(1,2), 2,1, byrow = TRUE)) plot(year,mort,main='Log NASDAQ index', xlab='Year',ylab=TeX('log (NASDAQ(t))'), type='l',cex.axis=1.1,cex.lab=1.1,lwd=3,col='red'); plot(year[1:length(diffxy)],diffxy, main='Cycle of Log NASDAQ index using Log-linear trend', xlab='Year',ylab=TeX('log-linear(NASDAQ(t))'),type='l', cex.axis=1.1,cex.lab=1.1,lwd=3,col='green'); layout(matrix(c(1,2,3,4), 4,1, byrow = TRUE)) plot(year,mort,main='Log NASDAQ index', xlab='Year',ylab=TeX('log (NASDAQ(t))'),type='l', col='red',cex.axis=1.1,cex.lab=1.1,lwd=3); plot(ac1,main='Autocorrelation of log NASDAQ HP Cycles', xlab='Lag',ylab='AC(1)') lines(ac1$lag,ac1$acf,main='Autocorrelation of log NASDAQ HP Cycles', xlab='Lag',ylab='AC(1)',type='l', col='blue',cex.axis=1.1,cex.lab=1.1,lwd=3) plot(bc1,main='Autocorrelation of log NASDAQ FD ', xlab='Lag',ylab='AC(1)') lines(bc1$lag,bc1$acf,main='Autocorrelation of log NASDAQ FD', xlab='Lag',ylab='AC(1)',type='l', col='blue',cex.axis=1.1,cex.lab=1.1,lwd=3) plot(dfacf,main='Autocorrelation of log-linear NASDAQ ', xlab='Lag',ylab='AC(1)') lines(dfacf$lag,dfacf$acf,main='Autocorrelation of log-linear NASDAQ ', xlab='Lag',ylab='AC(1)',type='l',col='blue',lwd=3) layout(matrix(c(1,2,3,4,5,6), 3, 2, byrow = TRUE)) #par(mfrow=c(2,1),mar=c(3,3,2,1),cex=.8) x=seq(-15,15,.1); y=sin(x) ac1=acf(y,lag.max=100,plot=FALSE); plot(x,y,main='Sin wave',xlab='T',ylab='Sin(t)',type='l', col='red',cex.axis=1.1,cex.lab=1.1,lwd=3) plot(ac1,main='Autocorrelation of Sin wave',xlab='Lag',ylab='AC(1)', cex.axis=1.1,cex.lab=1.1,lwd=.2) lines(ac1$lag,ac1$acf,type='l',col='blue',lwd=2) x=seq(-15,15,.1); y=x^2+x^3 ac1=acf(y,lag.max=100,plot=FALSE); plot(x,y,main='Polynomial', xlab='T',ylab=TeX('y=x^3(t)+x^2(t)'),type='l',col='red', cex.axis=1.1,cex.lab=1.1,lwd=3) plot(ac1,main='Autocorrelation of Polynomial', xlab='Lag',ylab='AC(1)',cex.axis=1.1,cex.lab=1.5,lwd=.2) lines(ac1$lag,ac1$acf,type='l',col='blue',lwd=2) x=seq(-15,15,.1); y=sin(x)*rnorm(length(x),mean=0,sd=1) ac1=acf(y,lag.max=100,plot=FALSE); plot(x,y,main='Sin wave with random noise', xlab='t', ylab=TeX('Sin(t) * r($\\mu=0 ,\\sigma^2=1)'),type='l',col='red', cex.axis=1.1,cex.lab=1.1,lwd=2) plot(ac1,main='Autocorrelation of Sin wave with random noise', xlab='Lag',ylab='AC(1)',cex.axis=1.1,cex.lab=1.5,lwd=3) lines(ac1$lag,ac1$acf,type='l',col='blue',lwd=.2) corrlength(ac1) sta.nasdaq=list(mean(dfacf$acf),sd(dfacf$acf),var(dfacf$acf),corrlength(dfacf), mean(ac1$acf),sd(ac1$acf),var(ac1$acf),corrlength(ac1), mean(bc1$acf),sd(bc1$acf),var(bc1$acf),corrlength(bc1)) print("Dtrend statistics for SP500") print(matrix(sta.sp500,nrow=4)) print("Dtrend statistics for NASDAQ") print(matrix(sta.nasdaq,nrow=4)) } corrlength <- function(acfvector) { ind=min(which(acfvector$acf<0)); return ((abs(acfvector$acf[ind])+abs(acfvector$acf[ind-1])/10)*(abs(acfvector$acf[ind-1]))+ind-1) }

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/INF-0612/aula3/aula3.R

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arthurlustosa/MDC-Unicamp

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refs/heads/master

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aula3.R

# Definindo Funcoes mysum <- function(a, b) { a + b } makePower <- function(n) { function(x) { x^n } } square <- makePower(2) cube <- makePower(3) subvector <- function(vector, begin = 1, end = length(vector)) { return(vector[begin:end]) } mydist <- function(x = c(0, 0), y = c(0, 0)) { sqrt((x[1] - y[1])^2 + (x[2] - y[2])^2) } truncd <- function(n, d = 0) { trunc(n * 10^d) / 10^d } # Comandos Condicionais odd <- function(x) { if (x %% 2 == 1) { TRUE } else { FALSE } } odd <- function(x) { ifelse(x %% 2 == 1, TRUE, FALSE) } odd <- function(x) { x %% 2 == 1 } myabs <- function(a) { if (a < 0) { -a } else { a } } bhaskara <- function(a = 0, b = 0, c = 0) { if (a != 0) { delta <- as.complex(b^2 - 4*a*c) if (delta != 0) { c((-b + sqrt(delta)) / (2 * a), (-b - sqrt(delta)) / (2 * a)) } else { -b / (2 * a) } } else { -c / b } } # Comandos de Repeticao printVector <- function(v) { i <- 1 while(i <= length(v)) { print(v[i]) i <- i + 1 } } printVector <- function(v) { for (i in v) { print(i) } } mysum <-function(...) { x <- 0 for (i in c(...)) { x <- x + i } return(x) } mylength <- function(vector) { k <- 0 for (i in vector) { k <- k + 1 } return(k) } mylength <- function(...) { k <- 0 for (i in c(...)) { k <- k + 1 } return(k) } multlength <- function(...) { result <- NULL for (i in list(...)) { result <- c(result, length(i)) } return(result) } multlength(25:30, matrix(1:12, 3, 4), rnorm(5), sample(10)) mymin <- function(...) { min <- Inf for (i in c(...)) { if (i < min) { min <- i } } return(min) } mymin <- function(...) { min <- Inf if (missing(...)) { warning("missing arguments; returning Inf") } else { for (i in c(...)) { if (i < min) { min <- i } } } return(min) } subset <- function(set1, set2) { all(is.element(set1, set2)) } subset <- function(set1, set2) { for (elem in set1) { if (!is.element(elem, set2)) { return(FALSE) } } return(TRUE) } index <- function(vector, element) { n <- length(vector) result <- NULL for (i in 1:n) { if (vector[i] == element) { result <- c(result, i) } } result } # lapply L <- list(a = 25:30, b = matrix(1:6, 2, 3), c = rnorm(5), d = sample(10)) lapply(L, mean) lapply(2:4, runif) lapply(2:4, runif, min = 0, max = 10) lapply(datasets::faithful, max) lapply(faithful, min) lapply(faithful, function(x) {max(x) - min(x)}) # sapply sapply(L, mean) sapply(L, range) lapply(faithful, range) lapply(faithful, quantile) sapply(faithful, quantile) # apply m <- matrix(sample(12), nrow = 3, ncol = 4); m apply(m, 1, min) apply(m, 2, max) m <- matrix(sample(8)) dim(m) <- c(2, 2, 2); m apply(m, 1, mean) apply(m[ , , 1], 1, mean) apply(m, 2, mean) apply(m[ , , 2], 2, mean) total <- sum(datasets::HairEyeColor); total apply(HairEyeColor, 1, sum) / total apply(HairEyeColor, 2, sum) / total apply(HairEyeColor, 3, sum) / total # mapply mapply(rep, 1:3, 5:3) mapply("^", 1:6, 2:3) tipo1 <- sample(10:99, 10); tipo1 tipo2 <- sample(10:99, 10); tipo2 tipo3 <- sample(10:99, 10); tipo3 tipo4 <- sample(10:99, 10); tipo4 mapply(min, tipo1, tipo2, tipo3, tipo4) mapply(max, tipo1, tipo2, tipo3, tipo4) # tapply x <- c(rnorm(100), runif(100), sample(100)) f <- gl(n = 3, k = 100, labels = c("norm", "unif", "sample")) tapply(x, f, range) s <- sample(length(x)) df <- data.frame(x[s], f[s]) tapply(df$x, df$f, range) tapply(datasets::mtcars$mpg, datasets::mtcars$cyl, mean) tapply(mtcars$qsec, mtcars$cyl, mean) tapply(mtcars$hp, mtcars$vs, mean) qfactor <- function(vector) { q <- quantile(vector) result <- NULL for (i in vector) if (i <= q["25%"]) result <- c(result, "q1") else if (i <= q["50%"]) result <- c(result, "q2") else if (i <= q["75%"]) result <- c(result, "q3") else result <- c(result, "q4") return(as.factor(result)) } tapply(mtcars$mpg, qfactor(mtcars$hp), mean) tapply(mtcars$mpg, qfactor(mtcars$qsec), max) tapply(mtcars$hp, qfactor(mtcars$mpg), mean) tapply(datasets::Loblolly$height, datasets::Loblolly$age, min) tapply(Loblolly$height, Loblolly$age, mean) tapply(Loblolly$height, Loblolly$age, max) tapply(datasets::airquality$Temp, datasets::airquality$Month, mean) tapply(airquality$Solar.R, airquality$Month, mean, na.rm = TRUE) tapply(airquality$Ozone, airquality$Month, mean, na.rm = TRUE) tapply(datasets::iris$Petal.Length, datasets::iris$Species, mean) tapply(iris$Petal.Width, iris$Species, mean) tapply(iris$Petal.Length / iris$Petal.Width, iris$Species, mean) tapply(iris$Petal.Length, iris$Species, summary) simplify2array(tapply(iris$Petal.Length, iris$Species, summary))

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/run.analysis.R

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JasonSklikas/Getting-and-cleaning-data-project

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refs/heads/master

2021-06-08T05:55:37.673805

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run.analysis.R

#download and unzip the file. if(!file.exists("./Project")){dir.create("./Project")} url<-"https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip" download.file(url,destfile = "./Project/dataset.zip") unzip("dataset.zip") #1.Merges the training and the test sets to create one data set. #Load the train info x_train<- read.table("train/x_train.txt") y_train<- read.table("train/y_train.txt") trainsubject<- read.table("train/subject_train.txt") #load the test info x_test<-read.table("test/x_test.txt") y_test<-read.table("test/y_test.txt") testsubject<-read.table("test/subject_test.txt") #Create the data sets xdata<- rbind(x_train,x_test) ydata<- rbind(y_train,y_test) subjectdata<- rbind(trainsubject,testsubject) # 2. Extracts only the measurements on the mean and standard deviation for each measurement. # #load the features features<- read.table("features.txt") #Extract only the mean and std mean_n_std_features<- grep(".*mean.*|.*std.*", features[,2]) ##Tidy x_data, set column names xdata<- xdata[, mean_n_std_features] names(xdata) <-features[mean_n_std_features,2] # 3. Uses descriptive activity names to name the activities in the data set #Load activities and tidy them activities<- read.table("activity_labels.txt") activities[,2]<- gsub("_"," ", tolower(as.character(activities[,2]))) names(xdata) <- gsub("-mean","mean",names(xdata)) #Remove -mean names(xdata) <- gsub("-std","std",names(xdata)) #Remove -std names(xdata) <- gsub("[()-]","",names(xdata)) names(xdata) <- tolower(names(xdata)) names(xdata) <- gsub("^t","time",names(xdata)) names(xdata) <- gsub("^f","frequency",names(xdata)) names(xdata) <- gsub("acc","accelerometer",names(xdata)) names(xdata) <- gsub("gyro","gyroscope",names(xdata)) names(xdata) <- gsub("bodybody","body",names(xdata)) names(xdata) <- gsub("mag","magnitude",names(xdata)) #replace 1-6 into the activities names,so ydata now has as values the activity names ydata[,1]<-activities[ydata[,1],2] #Set the column names activity and subject names(ydata)<- "activity" names(subjectdata)<-"subject" #Create the final data set clean_data<- cbind(subjectdata,ydata,xdata) write.table(clean_data, "clean_data.txt",row.name=FALSE) # 5. Creates a 2nd, independent tidy data set with the average of each variable for each activity and each subject. library(plyr) data2<-aggregate(.~subject+activity,clean_data,mean) data2<-data2[order(data2$subject,data2$activity), ] Average_data<-data2 write.table(Average_data,file = "tidy_data_average.txt", row.name=FALSE)

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/man/disp2D-package.Rd

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cran/disp2D

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disp2D-package.Rd

\name{disp2D-package} \alias{disp2D-package} \alias{disp2D} \docType{package} \title{ Hausdorff and Simplex Dispersion orderings } \description{ Given a 2D point set, different three point sets are selected. The Hausdorff distances between the convex hulls are calculated exactly. } \details{ \tabular{ll}{ Package: \tab disp2D\cr Type: \tab Package\cr Version: \tab 1.0\cr Date: \tab 2012-05-24\cr License: \tab GPL-2\cr LazyLoad: \tab yes\cr } } \author{ Guillermo Ayala <Guillermo.Ayala@uv.es> Maintainer: Guillermo Ayala } \references{ Ayala G. and Lopez M. The simplex dispersion ordering and its application to the evaluation of human corneal endothelia. Journal of Multivariate Analysis, 100:1447-1464, 2009. G. Ayala, M.C. Lopez-Diaz, M. Lopez-Diaz, and L. Martinez-Costa. Studying hypertension in ocular fundus images using Hausdorff dispersion ordering. Mathematical Medicine and Biology: A journal of the IMA, 2011. Miguel Lopez-Diaz. An indexed multivariate dispersion ordering based on the Hausdorff distance. Journal of Multivariate Analysis, 97(7):1623 - 1637, 2006. G. Ayala, M.C. Lopez-Diaz, M. Lopez-Diaz and L. Martinez-Costa. Methods and algorithms to test the simplex and Hausdorff dispersion orders with a simulation study and an Ophthalmological application. Technical Report. 2012 } \keyword{ package } \examples{ library(disp2D) library(geometry) library(mvtnorm) sigma1 = matrix(c(0.912897,1.092679,1.092679,1.336440),byrow=TRUE,ncol=2) sigma2 = sigma1 + diag(1,ncol=2,nrow=2) A = rmvnorm(200,mean=rep(0,2),sigma=sigma1) B = rmvnorm(200,mean=rep(0,2),sigma=sigma2) r=.1 prob = probA = probB = rep(1/200,200) HA = exactHausdorff(A,probA,r) HB = exactHausdorff(B,probB,r) plot(HA$distance, cumsum(HA$probability), type = "l", xlab = "", ylab = "DF", xlim = range(c(HA,HB))) lines(HB$distance, cumsum(HB$probability), lty = 2) d1 = simplex(A,bootstrap=TRUE,nresamples=100) }

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/Data Analysis 3 test file.R

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twgg201/datan3_2019

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Data Analysis 3 test file.R

#data analysis 3 test file

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/dataset_splits/stratify.R

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NUNLP/AD_modeling

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stratify.R

library(dplyr) library(magrittr) strat_sample <- function(data, gr_variab, tr_percent, seed) { stopifnot(tr_percent > 0 & tr_percent < 1) if(require(dplyr) & require(magrittr)) { if (!missing(seed)) set.seed(seed) names0 <- names(data) gr_variab <- which(names0 == gr_variab) names(data) <- make.unique(c("n", "tRows", "SET", names0))[-(1:3)] gr_variab <- names(data)[gr_variab] data %<>% sample_frac %>% group_by_(gr_variab) %>% mutate(n = n(), tRows = round(tr_percent * n)) data %<>% mutate(SET = ifelse(row_number() <= tRows, "Train", "Test")) %>% select(-n, -tRows) %>% ungroup names(data) <- make.unique(c(names0, "SET")) data } } extract_set <- function(data, whichSET) { stopifnot(is.element(whichSET, c("Train", "Test"))) if (require(dplyr)) { variab <- names(data)[ncol(data)] condit <- get(variab, data) == whichSET data %>% filter_(~ condit) %>% select_(paste0("-", variab)) } } ## example ## #n <- 1e+5 #set.seed(386) #Df <- data.frame(V1 = rnorm(n), # V2 = rt(n, df = 4), # V3 = rpois(n, lambda = 1), # y = sample(letters[1:4], n, replace = T, # prob = c(.33, .33, .33, .01))) #groups <- strat_sample(Df, "y", .75) #with(groups, prop.table(table(y, SET), 1)) # a tibble #extract_set(groups, "Train") #extract_set(groups, "Test") #samples <- strat_sample(dat_grouped_codes, "label", 0.8) #with(samples, prop.table(table(label, SET), 1)) # check! #train_set <- extract_set(samples, "Train") #test_set <- extract_set(samples, "Test")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/apputils.R \name{update_toastr_css} \alias{update_toastr_css} \title{Update shinytoastr css} \usage{ update_toastr_css(container = NULL, toast = NULL, rgba = NULL, hover.rgba = NULL, opacity = NULL, hover.opacity = NULL, radius = NULL, position = "top-center") } \arguments{ \item{container}{list of style arguments for the container div. See details and example.} \item{toast}{list of style arguments for the toast. See details and example.} \item{rgba}{numeric, vector of four css rgba property values for background color, e.g., \code{c(0, 0, 0, 0.5)}. See details.} \item{hover.rgba}{numeric, vector of four css rgba property values for background color on mouse hover. See details.} \item{opacity}{numeric, toast opacity. Appended to \code{container}.} \item{hover.opacity}{numeric, toast opacity on mouse hover.} \item{radius}{character, border radius, e.g., \code{"0px"}.} \item{position}{character, defaults to \code{"top-center"}.} } \value{ an html style tag. } \description{ Update toast css from shinytoastr package. } \details{ \code{apputils} already contains some toastr css overrides (loaded via \code{use_apputils}). This function allows for injecting additional or different css overrides for a specific toast container that may not already be as specified by \code{apputils}. This is typically used to adjust the app intro toast, hence the default for \code{position} is \code{"top-center"}. Note that list names and values may be quoted if necessary. See example. Should be familiar with source toastr css in addition to running the example in order to understand which elements apply to \code{container} vs. \code{toast}. If wanting to keep text fully opaque in the toast while using semi-transparency, especially useful when adding a background image, use css rgba instead of opacity. \code{rgba} and\code{hover.rgba} nullify opacity arguments if both are provided, respectively. } \examples{ update_toastr_css( list('overflow-y' = 'auto', width = '70\%', height = '700px'), list(top = '100px', margin = '0 auto', left = '115px') ) }

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## ## plot2.R ## ## Written by Richard Sobota as part of programming assignment ## in Exploratory Data Analysis course. ## ## Function uses packages dplyr and lubridate. ## plot2 <- function() { ## read data from file csv.data <- read.csv("household_power_consumption.txt", sep=";", na.strings=c("?"), stringsAsFactors=FALSE) ## use PNG file as graphics device png(file = "plot2.png") ## convert data to table tbl_df(csv.data) %>% ## convert date from string to Date mutate(Date = dmy(Date)) %>% ## select required interval filter(Date >= dmy("01/02/2007"), Date <= dmy("02/02/2007")) %>% ## add datetime mutate(datetime = Date + hms(Time)) %>% ## convert data to numbers where needed mutate(Global_active_power = as.numeric(Global_active_power)) %>% ## draw plot with(plot(datetime, Global_active_power, type="l", main="", ylab="Global Active Power (kilowatts)", xlab="")) dev.off() }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/algorithm-bk.R \name{lre_auto_bk} \alias{lre_auto_bk} \title{LRE solution method based on Blanchard and Kahn (1980, ECTA)} \usage{ lre_auto_bk(A, nx) } \arguments{ \item{A}{Square matrix} \item{nx}{The number of predetermined variables, \code{nx} is required by the algorithm.} } \value{ List of two functions (g, h), passed to \code{\link{simulate}} } \description{ This function solves for a linear policy function for the Linear Rational Expectations model of \deqn{(x_{t+1}, y_{t+1}) = A (x_{t}, y_{t})}{ (x_{t+1}, y_{t+1}) = A (x_{t}, y_{t})}, where x and y are predetermined and non-predetermined variables, respectively. }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/import.R \name{create_seurat_obj} \alias{create_seurat_obj} \title{Create a new Seurat object from a matrix.} \usage{ create_seurat_obj( counts_matrix, assay = "RNA", min_cells = 1, min_genes = 1, log_file = NULL, project = "proj" ) } \arguments{ \item{counts_matrix}{A matrix of raw counts.} \item{assay}{Seurat assay to add the data to.} \item{min_cells}{Include genes/features detected in at least this many cells.} \item{min_genes}{Include cells where at least this many genes/features are detected.} \item{log_file}{Filename for the logfile.} \item{project}{Project name for Seurat object.} } \value{ Seurat object. } \description{ Create a new Seurat object from a matrix. }

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order_dist_to_point.Rd.R

library(GpGp) ### Name: order_dist_to_point ### Title: Distance to specified point ordering ### Aliases: order_dist_to_point ### ** Examples n <- 100 # Number of locations d <- 2 # dimension of domain locs <- matrix( runif(n*d), n, d ) loc0 <- c(1/2,1/2) ord <- order_dist_to_point(locs,loc0)

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#' Discrete gamma distribution #' #' Probability mass function, distribution function and random generation #' for discrete gamma distribution. #' #' @param x,q vector of quantiles. #' @param n number of observations. If \code{length(n) > 1}, #' the length is taken to be the number required. #' @param rate an alternative way to specify the scale. #' @param shape,scale shape and scale parameters. Must be positive, scale strictly. #' @param log,log.p logical; if TRUE, probabilities p are given as log(p). #' @param lower.tail logical; if TRUE (default), probabilities are \eqn{P[X \le x]} #' otherwise, \eqn{P[X > x]}. #' #' @details #' #' Probability mass function of discrete gamma distribution \eqn{f_Y(y)}{f} #' is defined by discretization of continuous gamma distribution #' \eqn{f_Y(y) = S_X(y) - S_X(y+1)}{f(y) = S(x) - S(x+1)} #' where \eqn{S_X}{S} is a survival function of continuous gamma distribution. #' #' @references #' Chakraborty, S. and Chakravarty, D. (2012). #' Discrete Gamma distributions: Properties and parameter estimations. #' Communications in Statistics-Theory and Methods, 41(18), 3301-3324. #' #' @seealso \code{\link[stats]{GammaDist}}, \code{\link{DiscreteNormal}} #' #' @examples #' #' x <- rdgamma(1e5, 9, 1) #' xx <- 0:50 #' plot(prop.table(table(x))) #' lines(xx, ddgamma(xx, 9, 1), col = "red") #' hist(pdgamma(x, 9, 1)) #' plot(ecdf(x)) #' xx <- seq(0, 50, 0.1) #' lines(xx, pdgamma(xx, 9, 1), col = "red", lwd = 2, type = "s") #' #' @name DiscreteGamma #' @aliases DiscreteGamma #' @aliases ddgamma #' #' @keywords distribution #' @concept Univariate #' @concept Discrete #' #' @export ddgamma <- function(x, shape, rate = 1, scale = 1/rate, log = FALSE) { if (!missing(rate) && !missing(scale)) { if (abs(rate * scale - 1) < 1e-15) warning("specify 'rate' or 'scale' but not both") else stop("specify 'rate' or 'scale' but not both") } cpp_ddgamma(x, shape, scale, log[1L]) } #' @rdname DiscreteGamma #' @export pdgamma <- function(q, shape, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE) { pgamma(floor(q)+1, shape, scale = scale, lower.tail = lower.tail[1L], log.p = log.p[1L]) } #' @rdname DiscreteGamma #' @export rdgamma <- function(n, shape, rate = 1, scale = 1/rate) { floor(rgamma(n, shape, scale = scale)) }

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dvnorm_paper_v2.r

###nmprep.r ##Goal: To collate tables of missing data contained within nonclinical raw data obtained on 23rd March 2016 ##Note: Based heavily off of datacheck_cyt_script2.r -> Richards code # Remove any previous objects in the workspace rm(list=ls(all=TRUE)) graphics.off() # Set the working directory master.dir <- "E:/Hughes/Data" scriptname <- "nmprep_clin" setwd(master.dir) # Load libraries library(ggplot2) library(doBy) library(Hmisc) library(plyr) library(grid) library(reshape) library(stringr) library(scales) library(cowplot) library(gridExtra) # Source utility functions file source("E:/Hughes/functions_utility.r") # Customize ggplot2 theme - R 2.15.3 theme_bw2 <- theme_set(theme_bw(base_size = 22)) theme_bw2 <- theme_update(plot.margin = unit(c(1, 0.5, 3, 0.5), "lines"), axis.title.x = element_text(size = 18, vjust = 0), axis.title.y = element_text(size = 18, vjust = 0, angle = 90), strip.text.x = element_text(size = 16), strip.text.y = element_text(size = 16, angle = 90), legend.title = element_text(size = 18), legend.text = element_text(size = 16)) # Organise working and output directories working.dir <- paste(master.dir,"RAW_Clinical",sep="/") workspacefilename <- paste(getwd(),"/",scriptname,".RData", sep="") output.dir <- paste(working.dir,"/",scriptname,"_Output",sep="") if(!file.exists(output.dir)){ dir.create(output.dir) } filename <- paste(output.dir,"nmprep_flagged.csv",sep="/") nmprep <- read.csv(filename, na.strings = ".") locf <- function (x) { #Last observation carried forward #Finds an NA and carries forward the previous value good <- !is.na(x) positions <- seq(length(x)) good.positions <- good * positions last.good.position <- cummax(good.positions) last.good.position[last.good.position == 0] <- NA x[last.good.position] } nmprep$DOSE <- locf(nmprep$AMT) nmprep$DVNORM <- nmprep$DV/nmprep$DOSE bin_cuts <- c(0.52, 1.02, 2.02, 3.02, 5.02, 8.02, 49) nmprep$TADBIN <- cut2(nmprep$TAD, cuts = bin_cuts, levels.mean = T) levels(nmprep$TADBIN)[length(bin_cuts)] <- 24 nmprep$TADBIN <- as.numeric(paste(nmprep$TADBIN)) dose_bins <- c(8, 26, 80) nmprep$DOSEf <- cut2(nmprep$DOSE, cuts = dose_bins) levels(nmprep$DOSEf) <- c("<10mg", "10-25mg", ">25mg") nmprep$DXCATf <- factor(nmprep$DXCATNUM) levels(nmprep$DXCATf) <- c("CLL", "AML", "ALL", "MM") # Define colourblind palette cbPalette <- c("#0072B2", "#D55E00", "#009E73", "#CC79A7") # Create plot function dvnormPlot <- function(xCol, guideName) { p <- NULL p <- ggplot(aes(x = TAD, y = DVNORM*1000), data = nmprep) p <- p + geom_point(aes(colour = get(xCol)), alpha = 0.2) p <- p + stat_summary(aes(x = TADBIN, y = DVNORM*1000, colour = get(xCol)), fun.y = median, geom = "line", size = 1.2) p <- p + scale_y_log10(NULL, labels = comma) p <- p + scale_x_continuous(NULL, breaks = 0:6*4) p <- p + scale_colour_manual(name = guideName, values = cbPalette) p } # Create plots and use cowplot to create grid p1 <- dvnormPlot("DOSEf", "Dosage") p2 <- dvnormPlot("DXCATf", "Cancer") p3 <- plot_grid(p1, p2, align = "vh", labels = c("A", "B"), ncol = 1, hjust = -5) # Create text grobs for common y and x axis labels y.grob <- textGrob("Dose Normalised Concentrations (ng/mL)\n", vjust = 0.7, gp = gpar(fontface = "plain", col = "black", fontsize = 18), rot = 90) x.grob <- textGrob("Time After Last Dose (hours)", hjust = 0.6, vjust = -1, gp = gpar(fontface = "plain", col = "black", fontsize = 18)) # Produce final figure plot_grid(grid.arrange(arrangeGrob(p3, left = y.grob, bottom = x.grob))) ggsave("dvnormplot_v2.png", width = 17.4, height = 23.4, units = c("cm")) ggsave("dvnormplot_v2.eps", width = 17.4, height = 23.4, units = c("cm"), dpi = 1200, device = cairo_ps, fallback_resolution = 1200)

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if(F){ dfObs <- read.table("data/NISAR_obs.csv",stringsAsFactors=F,header=T,fileEncoding="UTF-8",sep=";") dfObs <- dfObs %>% filter(!ShapeID %in% c(168,169)) dfObs <- dfObs %>% filter(AphiaID != 127188) dfObs <- dfObs %>% filter(Source != "KU Fish") write.table(dfObs,file="data/NISAR_obs.csv",col.names=T,row.names=F,sep=";",na="",quote=T,fileEncoding="UTF-8") df1 <- dfObs %>% filter(Source=="MONIS-5") %>% distinct(Lat,Lon) %>% mutate(MID=row_number()) write.table(df1,file="../NISAR/20200512/monis5stns.csv", col.names=T,row.names=F,sep=";",na="",quote=T,fileEncoding="UTF-8") df2 <- read.table("../NISAR/20200512/monis5stnsRegions.csv", stringsAsFactors=F,header=T,fileEncoding="UTF-8",sep=";") %>% select(MID,REGIONID) df1 <- df1 %>% left_join(df2,by="MID") df1 <- df1 %>% select(-MID) %>% rename(REGIONIDfix=REGIONID) dfObs <- read.table("data/NISAR_obs.csv",stringsAsFactors=F,header=T,fileEncoding="UTF-8",sep=";") dfObs <- dfObs %>% left_join(df1,by=c("Lon","Lat")) dfObs <- dfObs %>% mutate(REGIONID=ifelse(is.na(REGIONIDfix),REGIONID,REGIONIDfix)) dfObs <- dfObs %>% select(-REGIONIDfix) write.table(dfObs,file="data/NISAR_obs.csv",col.names=T,row.names=F,sep=";",na="",quote=T,fileEncoding="UTF-8") }

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fig3_gsebias-sim-plots-tables.R

#!/usr/bin/env R # Author: Sean Maden # # Make heatmaps of GSE bias simulations results. library(ggplot2); library(data.table) library(scales); library(gridExtra) library(ggpubr) # library(magick) #---------- # load data #---------- # load data tables # sum of squared variances table msq.fname <- "msq-gse-bias_all-blood-2-platforms.rda" msq <- get(load(msq.fname)) # fev differences table mdif.fname <- "mdiff-gse-bias_all-blood-2-platforms.rda" mdif <- get(load(mdif.fname)) #--------------------------------------- # fraction explained variances plot data #--------------------------------------- dfp.fev <- apply(msq[,c(1:39)], 2, function(ci){ median(as.numeric(ci), na.rm=T)}) # format heatmap data lvlv <- c("gse", "predsex", "predcell.Mono", "predcell.NK", "predcell.CD4T", "predage", "predcell.Bcell", "predcell.CD8T", "predcell.Gran", "platform", "glint.epi.pc2", "glint.epi.pc1", "Residuals") dfp.fev <- data.frame(var = names(dfp.fev), value = as.numeric(dfp.fev)) dfp.fev$model <- gsub(".*_", "", dfp.fev$var) dfp.fev$var <- gsub("_.*", "", dfp.fev$var) dfp.fev$`Median\nFEV` <- as.numeric(dfp.fev$value) dfp.fev$var <- factor(dfp.fev$var, levels = lvlv) dfp.fev$model <- factor(dfp.fev$model, levels = c("unadj", "adj1", "adj2")) dfp.fev$value.label <- round(100*dfp.fev$value, digits = 2) #----------------------------------------------- # main fig -- compare var cat dist, violin plots #----------------------------------------------- # get plot data # get fev binned on type typev <- c("unadj", "adj1", "adj2") lvarv <- list(technical = c("platform", "gse"), demographic = c("predage", "predsex", "glint.epi.pc2", "glint.epi.pc1"), biological = c("predcell.CD8T", "predcell.CD4T", "predcell.NK", "predcell.Bcell", "predcell.Mono", "predcell.Gran")) msqf <- msq[,!grepl("^Residuals.*", colnames(msq))] ltot.fev <- lapply(typev, function(ti){apply(msqf[,grepl(ti, colnames(msqf))], 1, sum, na.rm = T)}) names(ltot.fev) <- typev # get plot data object dfp <- do.call(rbind, lapply(names(lvarv), function(vari){ varvii <- lvarv[[vari]] do.call(rbind, lapply(names(ltot.fev), function(ti){ fev.fract.denom <- ltot.fev[[ti]] msqff <- msqf[,grepl(ti, colnames(msqf))] # get vector of category ssq var which.cnamev <- grepl(paste0(varvii, collapse = "|"), colnames(msqff)) msqff <- msqff[, which.cnamev, drop = F] ssqv <- apply(msqff, 1, sum, na.rm = T) fev.cat.fractv <- ssqv/fev.fract.denom # get fraction fev by cat dfi <- data.frame(fev.fract = fev.cat.fractv) dfi$vartype <- vari dfi$modeltype <- ti return(dfi) })) })) # get plot objects dfp$`Model type` <- ifelse(dfp$modeltype=="unadj", "unadjusted", ifelse(dfp$modeltype=="adj1", "adjustment 1", "adjustment 2")) lvlv <- c("unadjusted", "adjustment 1", "adjustment 2") dfp$`Model type` <- factor(dfp$`Model type`, levels = lvlv) dfp$FEV <- dfp$fev.fract # format plot vars catv <- c("technical", "demographic", "biological") tech.str <- paste0(paste0(rep(" ", 13), collapse = ""), "Technical", collapse = "") biol.str <- paste0(paste0(rep(" ", 5), collapse = ""), "Biological", collapse = "") demo.str <- paste0(paste0(rep(" ", 2), collapse = ""), "Demographic", collapse = "") # get list of plot objects text.size <- 10; title.size <- 12 lgg <- lapply(catv, function(cati){ dfpi <- dfp[dfp$vartype == cati,] ggvp <- ggplot(dfpi, aes(y = FEV, x = `Model type`, fill = `Model type`)) + geom_violin(draw_quantiles = 0.5) + theme_bw() + theme(axis.text.x = element_blank(), axis.title.x = element_blank(), legend.position = "none", plot.title = element_text(size = title.size), axis.text.y = element_text(size = text.size)) if(cati == "biological"){ ggvp <- ggvp + ggtitle(biol.str) + theme(axis.title.y = element_text(size = title.size)) } if(cati == "demographic"){ ggvp <- ggvp + ggtitle(demo.str) + theme(axis.title.y = element_blank()) } if(cati == "technical"){ ggvp <- ggvp + ggtitle(tech.str) + theme(axis.title.y = element_blank()) } return(ggvp) }) names(lgg) <- catv # get zoom panel for technical # get plot legend pl <- ggplot(dfp, aes(y = FEV, x = `Model type`, fill = `Model type`)) + geom_violin(draw_quantiles = 0.5) + theme_bw() + theme(legend.title = element_text(size = title.size), legend.text = element_text(size = text.size)) lgg[["legend"]] <- get_legend(pl) # save new plot plot.fname <- "ggvp_fev-byvarcat_gsebias" mg.pgn.fname <- "magnifying_glass_bgtransparent.png" # get plot params lm <- matrix(c(1,1,2,2,3,3,4), nrow = 1) # save new pdf pdf(paste0(plot.fname, ".pdf"), 7.8, 1.8) grid.arrange(lgg[["biological"]], lgg[["demographic"]], lgg[["technical"]], lgg[["legend"]], layout_matrix = lm) dev.off() #-------------------- # sfigs, compare fevs #-------------------- # get plot data dfp1 <- data.frame(unadj = ltot.fev$unadj, adj.val = ltot.fev$adj1) dfp2 <- data.frame(unadj = ltot.fev$unadj, adj.val = ltot.fev$adj2) dfp1$adj.type <- "adj. 1"; dfp2$adj.type <- "adj. 2" dfp <- rbind(dfp1, dfp2) # fract fev dfp$fract.fev <- dfp$adj.val/dfp$unadj # plot scatterplot fev ggpt <- ggplot(dfp, aes(x = unadj, y = adj.val)) + geom_point(draw_quantiles = 0.1) + theme_bw() ggpt <- ggpt + facet_wrap(~adj.type, ncol = 2) pdf("ggpt_fev-adj-unadj_gsebias.pdf", 5.5, 3.5) print(ggpt); dev.off() # 2d density plot ggpt <- ggplot(dfp, aes(x = unadj, y = adj.val)) + geom_bin2d(bins = 70) + scale_fill_continuous(type = "viridis") + theme_bw() + xlab("Unadjusted FEV") + ylab("Adjusted FEV") + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) ggpt <- ggpt + facet_wrap(~adj.type, ncol = 2) pdf("ggdensity_fev-adj-unadj_gsebias.pdf", 3.5, 1.8) print(ggpt); dev.off() # plot fraction fev # get plot object ggvp <- ggplot(dfp, aes(x = adj.type, y = fract.fev, group = adj.type)) + geom_violin(show_quantiles = 0.5) + theme_bw() + ylab("FEV fraction\n(Adj./Unadj.)") + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1), axis.title.x = element_blank()) # save new plot pdf('ggvp_fev-fract_gsebias.pdf', 2.5, 1.5) print(ggvp);dev.off() # summary stats for reporting median(dfp[dfp$adj.type=="adj. 1",]$fract.fev) # 0.6882031 median(dfp[dfp$adj.type=="adj. 2",]$fract.fev) # 0.6841613 var(dfp[dfp$adj.type=="adj. 1",]$fract.fev) # 0.07646976 var(dfp[dfp$adj.type=="adj. 2",]$fract.fev) # 0.07648388 sd(dfp[dfp$adj.type=="adj. 1",]$fract.fev) # 0.2765317 sd(dfp[dfp$adj.type=="adj. 2",]$fract.fev) # 0.2765572 #---------------------------- # get data for fev dist plots #---------------------------- # get fev binned on type varv.technical <- c("platform") varv.dem <- c("predage", "predsex", "glint.epi.pc2", "glint.epi.pc1") varv.bio <- c("predcell.CD8T", "predcell.CD4T", "predcell.NK", "predcell.Bcell", "predcell.Mono", "predcell.Gran") lvarv <- list(technical = varv.technical, demographic = varv.dem, biological = varv.bio) typev <- c("unadj", "adj1", "adj2") # write to new results table dfp.fname <- "dfp_fev-bycat_gse-bias_blood-4stypes.csv" mcname <- matrix(c("vartype", "fev", "modeltype"), nrow = 1) data.table::fwrite(mcname, file = dfp.fname, sep = ",", row.names = F, col.names = F, append = F) # iterate on sims dfp <- do.call(rbind, lapply(seq(nrow(msq)), function(ri){ #message(ri); ridat <- msq[ri,] dfi <- do.call(rbind, lapply(typev, function(ti){ rii <- ridat[grepl(ti, names(ridat))] rii <- rii[!grepl('Residuals', names(rii))] total.var <- sum(as.numeric(rii), na.rm = T) do.call(rbind, lapply(names(lvarv), function(vi){ rii.vi <- rii[paste0(lvarv[[vi]], "_", ti)] if(!vi == "technical"){rii.vi <- sum(rii.vi, na.rm = T)} rfract <- as.numeric(rii.vi)/total.var data.frame(vartype = vi, fev = rfract, modeltype = ti) })) })) data.table::fwrite(dfi, file = dfp.fname, sep = ",", row.names = F, col.names = F, append = T) })) # save plot data dfp.fname <- "dfp_fev-bycat_gse-bias_blood-4stypes.rda" save(dfp, file = dfp.fname) #-------------------------------------- # table s2 -- median fevs by model, var #-------------------------------------- # get var categories grpv <- c("unadj", "adj1", "adj2") filtv <- c("biological", "demographic", "technical") tfev <- do.call(cbind, lapply(grpv, function(grpi){ dfpi <- dfp[dfp$modeltype==grpi,] unlist(lapply(filtv, function(filti){ dfpii <- dfpi[dfpi$vartype==filti,] median(dfpii$fev, na.rm = T) })) })) colnames(tfev) <- grpv rownames(tfev) <- filtv # get variable-wise fev dim(msq) msq.filt <- msq[,!grepl("^Residuals.*", colnames(msq))] dim(msq.filt) # total vars by sim ltot.fev <- lapply(grpv, function(grpi){ apply(msq.filt[,grepl(grpi, colnames(msq.filt))], 1, function(ri){sum(ri, na.rm = T)}) }) names(ltot.fev) <- grpv # parse fevs by var filtv <- unique(gsub("_.*", "", colnames(msq.filt)[1:36])) tfev.bind <- do.call(cbind, lapply(grpv, function(grpi){ msqi <- msq.filt[,grepl(grpi, colnames(msq.filt))] tot.fevi <- ltot.fev[[grpi]] unlist(lapply(filtv, function(filti){ fractvi <- msqi[,grepl(filti, colnames(msqi))]/tot.fevi median(fractvi, na.rm = T) })) })) colnames(tfev.bind) <- grpv rownames(tfev.bind) <- filtv # bind all results st2 <- rbind(tfev, tfev.bind) t(round(st2, 3)) #--------------------------------- # violin plots with technical zoom -- OLD #--------------------------------- source("facet_zoom2.R") # library(png) catv <- c("technical", "demographic", "biological") tech.str <- paste0(paste0(rep(" ", 13), collapse = ""), "Technical", collapse = "") biol.str <- paste0(paste0(rep(" ", 5), collapse = ""), "Biological", collapse = "") demo.str <- paste0(paste0(rep(" ", 2), collapse = ""), "Demographic", collapse = "") text.size <- 10 title.size <- 12 lgg <- lapply(catv, function(cati){ dfpi <- dfp[dfp$vartype == cati,] ggvp <- ggplot(dfpi, aes(y = FEV, x = `Model type`, fill = `Model type`)) + geom_violin(draw_quantiles = 0.5) + theme_bw() + theme(axis.text.x = element_blank(), axis.title.x = element_blank(), legend.position = "none", plot.title = element_text(size = title.size), axis.text.y = element_text(size = text.size)) if(cati == "biological"){ ggvp <- ggvp + ggtitle(biol.str) + theme(axis.title.y = element_text(size = title.size)) } if(cati == "demographic"){ ggvp <- ggvp + ggtitle(demo.str) + theme(axis.title.y = element_blank()) } if(cati == "technical"){ ggvp <- ggvp + ggtitle(tech.str) + theme(axis.title.y = element_blank()) + facet_zoom2(ylim = c(0, 0.01)) } return(ggvp) }) names(lgg) <- catv # get zoom panel for technical # get plot legend pl <- ggplot(dfp, aes(y = FEV, x = `Model type`, fill = `Model type`)) + geom_violin(draw_quantiles = 0.5) + theme_bw() + theme(legend.title = element_text(size = title.size), legend.text = element_text(size = text.size)) lgg[["legend"]] <- get_legend(pl) # save new plot plot.fname <- "ggviolin_fev-byvarcat_gsebias" mg.pgn.fname <- "magnifying_glass_bgtransparent.png" # get plot params lm <- matrix(c(1,1,1,1,1,1,1,1,1, 2,2,2,2,2,2,2,2, 3,3,3,3,3,3,3,3,3,3,3,3,3, 4,4,4,4,4,4), nrow = 1) # save new pdf pdf(paste0(plot.fname, ".pdf"), 7.8, 1.8) grid.arrange(lgg[["biological"]], lgg[["demographic"]], lgg[["technical"]], lgg[["legend"]], layout_matrix = lm) dev.off() # Produce image using graphics device # fig <- image_graph(width = 800, height = 200, res = 110) # ggplot2::qplot(mpg, wt, data = mtcars, colour = cyl) #grid.arrange(lgg[["biological"]], lgg[["demographic"]], lgg[["technical"]], # lgg[["legend"]], layout_matrix = lm) #dev.off() #mg.image <- image_scale(image_read(mg.pgn.fname), "x22") #out <- image_composite(fig, mg.image, # offset = geometry_point(475, -175)) #print(out)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vda.r \name{vda} \alias{vda} \title{Vargha and Delaney's A} \usage{ vda( formula = NULL, data = NULL, x = NULL, y = NULL, ci = FALSE, conf = 0.95, type = "perc", R = 1000, histogram = FALSE, reportIncomplete = FALSE, brute = FALSE, verbose = FALSE, digits = 3, ... ) } \arguments{ \item{formula}{A formula indicating the response variable and the independent variable. e.g. y ~ group.} \item{data}{The data frame to use.} \item{x}{If no formula is given, the response variable for one group.} \item{y}{The response variable for the other group.} \item{ci}{If \code{TRUE}, returns confidence intervals by bootstrap. May be slow.} \item{conf}{The level for the confidence interval.} \item{type}{The type of confidence interval to use. Can be any of "\code{norm}", "\code{basic}", "\code{perc}", or "\code{bca}". Passed to \code{boot.ci}.} \item{R}{The number of replications to use for bootstrap.} \item{histogram}{If \code{TRUE}, produces a histogram of bootstrapped values.} \item{reportIncomplete}{If \code{FALSE} (the default), \code{NA} will be reported in cases where there are instances of the calculation of the statistic failing during the bootstrap procedure.} \item{brute}{If \code{FALSE}, the default, the statistic is based on the U statistic from the \code{wilcox.test} function. If \code{TRUE}, the function will compare values in the two samples directly.} \item{verbose}{If \code{TRUE}, reports the proportion of ties and the proportions of (Ya > Yb) and (Ya < Yb).} \item{digits}{The number of significant digits in the output.} \item{...}{Additional arguments passed to the \code{wilcox.test} function.} } \value{ A single statistic, VDA. Or a small data frame consisting of VDA, and the lower and upper confidence limits. } \description{ Calculates Vargha and Delaney's A (VDA) with confidence intervals by bootstrap } \details{ VDA is an effect size statistic appropriate in cases where a Wilcoxon-Mann-Whitney test might be used. It ranges from 0 to 1, with 0.5 indicating stochastic equality, and 1 indicating that the first group dominates the second. By default, the function calculates VDA from the "W" U statistic from the \code{wilcox.test} function. Specifically, \code{VDA = U/(n1*n2)}. The input should include either \code{formula} and \code{data}; or \code{x}, and \code{y}. If there are more than two groups, only the first two groups are used. Currently, the function makes no provisions for \code{NA} values in the data. It is recommended that \code{NA}s be removed beforehand. When the data in the first group are greater than in the second group, VDA is greater than 0.5. When the data in the second group are greater than in the first group, VDA is less than 0.5. Be cautious with this interpretation, as R will alphabetize groups in the formula interface if the grouping variable is not already a factor. When VDA is close to 0 or close to 1, or with small sample size, the confidence intervals determined by this method may not be reliable, or the procedure may fail. } \note{ The parsing of the formula is simplistic. The first variable on the left side is used as the measurement variable. The first variable on the right side is used for the grouping variable. } \examples{ data(Catbus) vda(Steps ~ Gender, data=Catbus) } \references{ \url{http://rcompanion.org/handbook/F_04.html} } \seealso{ \code{\link{cliffDelta}}, \code{\link{multiVDA}} } \author{ Salvatore Mangiafico, \email{mangiafico@njaes.rutgers.edu} } \concept{Vargha and Delaney's A} \concept{Wilcoxon-Mann-Whitney} \concept{confidence interval} \concept{effect size}

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library(kmconfband) ### Name: noe.compute.cgh ### Title: Intermediate Steps in the Noe Recursions for the Exact Coverage ### Probability of a Nonparametric Confidence Band for the Survivor ### Function ### Aliases: noe.compute.cgh ### ** Examples ## Check of Noe recursion calculations. a<-c(0.001340,0.028958,0.114653,0.335379) b<-c(0.664621,0.885347,0.971042,0.998660) noe.compute.cgh(4,a,b)

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?install.packages old.packages() head(iris,n=10) summary(iris) var(iris$Sepal.Length) (v<-c(1,3,4,6)) a<-v[c(1,2,3)] a v v[v>2] v>2 v[-1] v[-3] v[-lenghth(v)] v[-length(v)] data=read.table(header=T, text=' subject sex size 1 M 7 2 F 6 3 M 11 ') data data[1,3] data data[1:2,] data[data$subject <3] (b<-4) v subset(data, subject<3, select=-subject) data subset(data, subject<3 & sex=="M")

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#' Submit a query and return results #' #' This function can handle REST API connections or JDBC connections. There is a benefit to #' calling this function for JDBC connections vs a straight call to \code{dbGetQuery()} in #' that the function result is a `tbl_df` vs a plain \code{data.frame} so you get better #' default printing (which can be helpful if you accidentally execute a query and the result #' set is huge). #' #' @param drill_con drill server connection object setup by \code{drill_connection()} or #' \code{drill_jdbc()}) #' @param query query to run #' @param uplift automatically run \code{drill_uplift()} on the result? (default: \code{TRUE}, #' ignored if \code{drill_con} is a \code{JDBCConnection} created by #' \code{drill_jdbc()}) #' @param .progress if \code{TRUE} (default if in an interactive session) then ask #' \code{httr::RETRY} to display a progress bar #' @references \href{https://drill.apache.org/docs/}{Drill documentation} #' @family Drill direct REST API Interface #' @export #' @examples #' try({ #' drill_connection() %>% #' drill_query("SELECT * FROM cp.`employee.json` limit 5") #' }, silent=TRUE) drill_query <- function(drill_con, query, uplift=TRUE, .progress=interactive()) { query <- trimws(query) query <- gsub(";$", "", query) if (inherits(drill_con, "JDBCConnection")) { try_require("rJava") try_require("RJDBC") try_require("sergeant.caffeinated") tibble::as_tibble(dbGetQuery(drill_con, query)) } else { drill_server <- make_server(drill_con) if (.progress) { httr::RETRY( verb = "POST", url = sprintf("%s/query.json", drill_server), encode = "json", httr::progress(), body = list( queryType = "SQL", query = query ), terminate_on = c(403, 404) ) -> res } else { httr::RETRY( verb = "POST", url = sprintf("%s/query.json", drill_server), encode = "json", body = list( queryType = "SQL", query = query ), terminate_on = c(403, 404) ) -> res } jsonlite::fromJSON( httr::content(res, as="text", encoding="UTF-8"), flatten=TRUE ) -> out if ("errorMessage" %in% names(out)) { message(sprintf("Query ==> %s\n%s\n", gsub("[\r\n]", " ", query), out$errorMessage)) invisible(out) } else { if (uplift) out <- drill_uplift(out) out } } } #' Turn columnar query results into a type-converted tbl #' #' If you know the result of `drill_query()` will be a data frame, then #' you can pipe it to this function to pull out `rows` and automatically #' type-convert it. #' #' Not really intended to be called directly, but useful if you accidentally ran #' \code{drill_query()} without `uplift=TRUE` but want to then convert the structure. #' #' @param query_result the result of a call to `drill_query()` #' @references \href{https://drill.apache.org/docs/}{Drill documentation} #' @export drill_uplift <- function(query_result) { if (length(query_result$columns) != 0) { query_result$rows <- query_result$rows[,query_result$columns,drop=FALSE] } if (length(query_result$columns) != 0) { if (is.data.frame(query_result$rows)) { if (nrow(query_result$rows) > 0) { query_result$rows <- query_result$rows[,query_result$columns,drop=FALSE] } } else { lapply(1:length(query_result$columns), function(col_idx) { ctype <- query_result$metadata[col_idx] if (ctype == "INT") { integer(0) } else if (ctype == "VARCHAR") { character(0) } else if (ctype == "TIMESTAMP") { cx <- integer(0) class(cx) <- "POSIXct" cx } else if (ctype == "BIGINT") { integer64(0) } else if (ctype == "BINARY") { character(0) } else if (ctype == "BOOLEAN") { logical(0) } else if (ctype == "DATE") { cx <- integer(0) class(cx) <- "Date" cx } else if (ctype == "FLOAT") { numeric(0) } else if (ctype == "DOUBLE") { double(0) } else if (ctype == "TIME") { character(0) } else if (ctype == "INTERVAL") { character(0) } else { character(0) } }) -> xdf xdf <- set_names(xdf, query_result$columns) class(xdf) <- c("data.frame") return(xdf) } } else { xdf <- dplyr::tibble() return(xdf) } # ** only available in Drill 1.15.0+ ** # be smarter about type conversion now that the REST API provides # the necessary metadata if (length(query_result$metadata)) { if ("BIGINT" %in% query_result$metadata) { if (!.pkgenv$bigint_warn_once) { if (getOption("sergeant.bigint.warnonce", TRUE)) { warning( "One or more columns are of type BIGINT. ", "The sergeant package currently uses jsonlite::fromJSON() ", "to process Drill REST API result sets. Since jsonlite does not ", "support 64-bit integers BIGINT columns are initially converted ", "to numeric since that's how jsonlite::fromJSON() works. This is ", "problematic for many reasons, including trying to use 'dplyr' idioms ", "with said converted BIGINT-to-numeric columns. It is recommended that ", "you 'CAST' BIGINT columns to 'VARCHAR' prior to working with them from ", "R/'dplyr'.\n\n", "If you really need BIGINT/integer64 support, consider using the ", "R ODBC interface to Apache Drill with the MapR ODBC drivers.\n\n", "This informational warning will only be shown once per R session and ", "you can disable them from appearing by setting the 'sergeant.bigint.warnonce' ", "option to 'FALSE' (i.e. options(sergeant.bigint.warnonce = FALSE)).", call.=FALSE ) } .pkgenv$bigint_warn_once <- TRUE } } sapply(1:length(query_result$columns), function(col_idx) { cname <- query_result$columns[col_idx] ctype <- query_result$metadata[col_idx] case_when( ctype == "INT" ~ "i", ctype == "VARCHAR" ~ "c", ctype == "TIMESTAMP" ~ "?", ctype == "BIGINT" ~ "?", ctype == "BINARY" ~ "c", ctype == "BOOLEAN" ~ "l", ctype == "DATE" ~ "?", ctype == "FLOAT" ~ "d", ctype == "DOUBLE" ~ "d", ctype == "TIME" ~ "c", ctype == "INTERVAL" ~ "?", TRUE ~ "?" ) }) -> col_types suppressMessages( tibble::as_tibble( readr::type_convert( df = query_result$rows, col_types = paste0(col_types, collapse=""), na = character() ) ) ) -> xdf } else { suppressMessages( tibble::as_tibble( readr::type_convert(df = query_result$rows, na = character()) ) ) -> xdf } xdf }

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/A549/scripts/chris/NB_balance/analysis_GainLossNB_genes.R

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no_license

ArnaudDroitLab/sb_cofactor

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77e5c922ecae7b4c66f140f5525085ab03344ec1

refs/heads/master

2021-01-23T05:29:40.591863

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analysis_GainLossNB_genes.R

# setwd("/Users/chris/Desktop/sb_cofactor_hr/A549") setwd("/home/chris/Bureau/sb_cofactor_hr/A549") source("scripts/ckn_utils.R") library(ChIPseeker) # Loading peaks peaks_dir <- "output/chip-pipeline-GRCh38/peak_call/A549_NB" gainNB_ovGR <- rtracklayer::import(con = file.path(peaks_dir, "NB_DEX_to_None_CTRL_ovGR_hg38.bed")); print(length(gainNB_ovGR)) # 399 gainNB_notovGR <- rtracklayer::import(con = file.path(peaks_dir, "NB_DEX_to_None_CTRL_notovGR_hg38.bed")); print(length(gainNB_notovGR)) # 3 lossNB_ovGR <- rtracklayer::import(con = file.path(peaks_dir, "NB_CTRL_to_None_DEX_ovGR_hg38.bed")); print(length(lossNB_ovGR)) # 561 lossNB_notovGR <- rtracklayer::import(con = file.path(peaks_dir, "NB_CTRL_to_None_DEX_notovGR_hg38.bed")); print(length(lossNB_notovGR)) # 904 # Width summary(width(gainNB_ovGR)) hist(width(gainNB_ovGR), breaks = 60) summary(width(gainNB_notovGR)) hist(width(gainNB_notovGR), breaks = 60) summary(width(lossNB_ovGR)) hist(width(lossNB_ovGR), breaks = 60) summary(width(lossNB_notovGR)) hist(width(lossNB_notovGR), breaks = 60) # Annotation gainNB_ovGR_annodf <- annotatePeaks(gainNB_ovGR, output = "df") gainNB_notovGR_annodf <- annotatePeaks(gainNB_notovGR, output = "df") lossNB_ovGR_annodf <- annotatePeaks(lossNB_ovGR, output = "df") lossNB_notovGR_annodf <- annotatePeaks(lossNB_notovGR, output = "df") # Retrieve genes which gain or lose NBC at the promoters geneGainNB_ovGR <- gainNB_ovGR_annodf %>% filter(Annot %in% c("Promoter")) %>% pull(geneId) %>% unique geneGainNB_notovGR <- gainNB_notovGR_annodf %>% filter(Annot == "Promoter") %>% pull(geneId) %>% unique geneLossNB_ovGR <- lossNB_ovGR_annodf %>% filter(Annot == "Promoter") %>% pull(geneId) %>% unique geneLossNB_notovGR <- lossNB_notovGR_annodf %>% filter(Annot == "Promoter") %>% pull(geneId) %>% unique symbol_all_geneGainNB_ovGR <- gainNB_ovGR_annodf %>% pull(SYMBOL) %>% unique symbol_all_geneGainNB_notovGR <- gainNB_notovGR_annodf %>% pull(SYMBOL) %>% unique symbol_all_geneLossNB_ovGR <- lossNB_ovGR_annodf %>% pull(SYMBOL) %>% unique symbol_all_geneLossNB_notovGR <- lossNB_notovGR_annodf %>% pull(SYMBOL) %>% unique symbol_prom_geneGainNB_ovGR <- gainNB_ovGR_annodf %>% filter(Annot == "Promoter") %>% pull(SYMBOL) %>% unique symbol_prom_geneGainNB_notovGR <- gainNB_notovGR_annodf %>% filter(Annot == "Promoter") %>% pull(SYMBOL) %>% unique symbol_prom_geneLossNB_ovGR <- lossNB_ovGR_annodf %>% filter(Annot == "Promoter") %>% pull(SYMBOL) %>% unique symbol_prom_geneLossNB_notovGR <- lossNB_notovGR_annodf %>% filter(Annot == "Promoter") %>% pull(SYMBOL) %>% unique ######### upDEX <- c("PER1", "ZFP36", "ERRFI1", "ANGPTL4", "NR1D2", "CRY2") upDEX_in_gainNB <- upDEX %in% symbol_all_geneGainNB_ovGR; names(upDEX_in_gainNB) <- upDEX upDEX_in_gainNB downDEX <- c("IL11") downDEX_in_gainNB <- downDEX %in% symbol_all_geneGainNB_ovGR; names(downDEX_in_gainNB) <- downDEX downDEX_in_gainNB ###################### # Draw FC time series ###################### source("scripts/reddy_time_series/draw_graph_log2FC_0-12h.R") geneGroupList <- list("GainNB_ovGR_withGR" = geneGainNB_ovGR, "GainNB_notovGR_withGR" = geneGainNB_notovGR, "LossNB_withGR" = geneLossNB_ovGR, "LossNB_withoutGR" = geneLossNB_notovGR) draw_time_course_FC(geneGainNB_ovGR) draw_time_course_FC(gainNB_ovGR_annodf %>% pull(geneId) %>% unique) draw_time_course_FC(geneGainNB_notovGR) draw_time_course_FC(geneLossNB_ovGR) draw_time_course_FC(geneLossNB_notovGR) draw_time_course_pergroup_FC(geneGroupList) # geneLossNBC_ovGR: Action répressive de GR par binding direct # geneLossNBC_notovGR: Les premières observations ne montre pas de grand changements dans le niveau de fold change de gene expression, réservoir de cofacteurs? gainNB_ovGR_annodf %>% filter(distanceToTSS > 500000)

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/RealData/Code/JSMultistateInfFunctions.R

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angieluis/BayesianMarkRecapSNV

51a6428770cb39202c090b4a287008f00ab74916

b67a59194b3282cd41f3b13946a7437eecc46eb8

refs/heads/master

2023-02-16T15:10:48.702511

2023-02-10T20:16:33

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JSMultistateInfFunctions.R

logit=function(x){ log(x/(1-x))} revlogit=function(x){ exp(x)/(1+exp(x))} # function to create a primary CH from the secondary capture history: primaryMS.fun<-function(CH.secondary){ x <- lapply(CH.secondary,function(x){apply(x,1,min)}) v1 <- unlist(x) CH.primary <- matrix(v1, nrow=dim(CH.secondary[[1]])[1], ncol=length(CH.secondary)) return(CH.primary) } #functions to add dummy occasion primary.dummy.fun <- function(CH.primary,notseen=3){ CH.primary.du <- cbind(rep(notseen, dim(CH.primary)[1]), CH.primary) return(CH.primary.du) } secondary.dummy.fun <- function(CH.secondary,notseen=3){ CH.secondary.du <- c(list(matrix(notseen,nrow=dim(CH.secondary[[1]])[1],ncol=dim(CH.secondary[[1]])[2])), CH.secondary) return(CH.secondary.du) } # functions to Augment data primary.augment.fun <- function(CH.primary.du,notseen=3,num.aug=500){ nz <- num.aug CH.primary.ms <- rbind(CH.primary.du, matrix(notseen, ncol = dim(CH.primary.du)[2], nrow = nz)) return(CH.primary.ms) } secondary.augment.fun <- function(CH.secondary.du,notseen=3,num.aug=500){ nz <- num.aug CH.secondary.ms <- lapply(CH.secondary.du,function(x){rbind(x,matrix(notseen, ncol = dim(x)[2], nrow = nz))}) return(CH.secondary.ms) } # Function to create known latent states z ### fill in all known but unobserved states (can't go back to S from I) #If observed as I, then not seen, and seen again later, when not seen must have been I. #If observed as S, not seen, then observed as S again, then must be S. # Remember these are now states not observations, so coded differently. # 1 is not yet entered # 2 is S # 3 is I # 4 is dead # only able to fill in 2's and 3's # Allows us to fill in a lot. And should speed up computation time known.state.SImsJS <- function(ms=CH.primary.ms, notseen=3){ # ms is multistate capture history # notseen: label for 'not seen' #here is 3 state <- ms state[state==notseen] <- NA for(i in 1:dim(ms)[1]){ if(length(which(ms[i, ] == 2)) > 0){ #filling in I's where can minI <- min(which(ms[i, ] == 2)) #I's are observation 2 maxI <- max(which(ms[i, ] == 2)) state[i, minI:maxI] <- 3} # I's are state 3 if(length(which(ms[i, ]==1)) > 0){ #filling in S's where can minS <- min(which(ms[i, ] == 1)) # S's are observation 1 maxS <- max(which(ms[i, ] == 1)) state[i, minS:maxS] <- 2} # S's are state 2 } return(state) } # Specify initial values jsmsinf.init <- function(ch=CH.primary.ms, num.aug=500){ # ch is primary capture histories after augmentation # nz is number of rows added for augmentation nz <- num.aug kn.state <- known.state.SImsJS(ms=ch) state <- matrix(2, nrow=dim(ch)[1], ncol=dim(ch)[2]) # default is S (2) state <- replace(state,!is.na(kn.state),NA) for(i in 1:(dim(state)[1]-nz)){ f <- min(which(is.na(state[i,]))) # before ever caught if(f>1){state[i,1:(f-1)] <- 2} # tried both 1 and 2 here, still get errors if(length(which(kn.state[i,] == 3)) > 0){ maxI <- max(which(kn.state[i,]==3)) if(maxI<dim(state)[2] ){ state[i,(maxI+1):dim(state)[2]] <- 3 # all after caught as I are I (3) } } } state[(dim(state)[1]-nz+1):dim(state)[1],] <- 1 state[,1] <- NA #this is specified in likelihood return(state) }

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/data/genthat_extracted_code/RCircos/examples/RCircos.Get.Heatmap.Color.Scale.Rd.R

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surayaaramli/typeRrh

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refs/heads/master

2023-05-05T04:05:31.617869

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RCircos.Get.Heatmap.Color.Scale.Rd.R

library(RCircos) ### Name: RCircos.Get.Heatmap.Color.Scale ### Title: Generate Color Scales for Heatmap Plot ### Aliases: RCircos.Get.Heatmap.Color.Scale ### Keywords: methods ### ** Examples library(RCircos) colorScales <- RCircos.Get.Heatmap.Color.Scale(heatmap.color="BlueWhiteRed")

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/oldScripts/aylinsMonster/Franken_SPACE_5-4-15.R

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TinasheMTapera/Reward

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refs/heads/master

2023-08-19T20:23:37.374939

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Franken_SPACE_5-4-15.R

### converts NSRS, FRANKEN & FRANKEN ADOL. item-level data from giant redcap project (Wolf Satterthwaite Repository) into New Franken Space ### # updated on 2/1/16 to run from Selkie server instead of Banshee # records must be updated on selkie from banshee for active projects (like effort) before it is run otherwise the output won't be the most up-to-date library("bitops") library("RCurl") library("REDCapR") set_config(config(ssl_verifypeer = 0L)) set_config(config(sslversion = 1)) #Create a redcap.cfg file in Users directory with ALL User-Projects and User-specific Tokens redcap_uri <- "https://selkie.uphs.upenn.edu/API/" ALL_Projects<-read.csv("~/.redcap.cfg") #List of projects needed for full data import projects<-ALL_Projects[which(ALL_Projects[,1] == "Wolf Satterthwaite Repository"),] ####Importing selected Selkie Redcap Project and Dictionary#### i<-1 p.token<-projects[i,2] name<-projects[i,1] #print(p.token) #print(name) project_data<-redcap_read_rdh( redcap_uri = redcap_uri, token = p.token, #config_options = list(ssl.verifypeer=FALSE), commented out for this version of R but may be required in future batch=1000 )$data project_dictionary<-redcap_metadata_read(redcap_uri=redcap_uri, token=p.token)$data ##### measure="nsrs" measure_data<-project_data[which(project_data$procedure==measure), c(project_dictionary$field_name[which(project_dictionary$form_name %in% c("general",measure))])] giant1<-measure_data giant1[giant1 ==-9999] <- NA measure="franken" measure_data<-project_data[which(project_data$procedure==measure), c(project_dictionary$field_name[which(project_dictionary$form_name %in% c("general",measure))])] giant2<-measure_data giant2[giant2 ==-9999] <- NA measure="frankenadol" measure_data<-project_data[which(project_data$procedure==measure), c(project_dictionary$field_name[which(project_dictionary$form_name %in% c("general",measure))])] giant3<-measure_data giant3[giant3 ==-9999] <- NA giant1<-giant1[order(giant1$bblid),] giant2<-giant2[order(giant2$bblid),] giant3<-giant3[order(giant3$bblid),] nsrs<-giant1[,c('participant_id','bblid', grep('nsrs[1-9]', names(giant1), value=T)),drop=F] franken<-giant2[,c('participant_id','bblid', grep('franken_[1-9]', names(giant2), value=T)),drop=F] frankenadol<-giant3[,c('participant_id','bblid', grep('franken_a_[1-9]', names(giant3), value=T)),drop=F] #merge all three data frames together df1<-merge(nsrs,franken, by=c("participant_id","bblid"), all.x=T, all.y=T) NEW_frank<-merge(df1,frankenadol, by=c("participant_id","bblid"), all.x=T, all.y=T) NEW_frank$frankenversion<-ifelse(is.na(NEW_frank[,c("franken_1")] & is.na(NEW_frank[,c("franken_8")])),NA,"franken") #determines version that the scores come from NEW_frank$frankenversion<-ifelse(is.na(NEW_frank[,c("frankenversion")]) & ! is.na(NEW_frank[,c("nsrs1")]),"nsrs",NEW_frank$frankenversion) #determines version that the scores come from NEW_frank$frankenversion<-ifelse(is.na(NEW_frank[,c("frankenversion")]) & ! is.na(NEW_frank[,c("franken_a_1")]),"frankenadol",NEW_frank$frankenversion) #determines version that the scores come from NEW_frank$newfrank1<-ifelse(is.na(NEW_frank[,c("franken_1")]), NEW_frank[,c("nsrs1")], NEW_frank[,c("franken_1")]) #if franken is NA, then use nsrs score, else use franken score NEW_frank$newfrank1<-ifelse(is.na(NEW_frank[,c("newfrank1")]), NEW_frank[,c("franken_a_1")], NEW_frank[,c("newfrank1")]) #if nsrs score is NA, then use frankenadol score, else use nsrs score NEW_frank$newfrank2<-ifelse(is.na(NEW_frank[,c("franken_2")]), NEW_frank[,c("nsrs2")], NEW_frank[,c("franken_2")]) NEW_frank$newfrank2<-ifelse(is.na(NEW_frank[,c("newfrank2")]), NEW_frank[,c("franken_a_2")], NEW_frank[,c("newfrank2")]) NEW_frank$newfrank3<-ifelse(is.na(NEW_frank[,c("franken_3")]), NEW_frank[,c("nsrs3")], NEW_frank[,c("franken_3")]) NEW_frank$newfrank3<-ifelse(is.na(NEW_frank[,c("newfrank3")]), NEW_frank[,c("franken_a_3")], NEW_frank[,c("newfrank3")]) NEW_frank$newfrank4<-ifelse(is.na(NEW_frank[,c("franken_4")]), NEW_frank[,c("franken_a_4")], NEW_frank[,c("franken_4")]) NEW_frank$newfrank5<-ifelse(is.na(NEW_frank[,c("franken_4a")]), NEW_frank[,c("nsrs8")], NEW_frank[,c("franken_4a")]) NEW_frank$newfrank5<-ifelse(is.na(NEW_frank[,c("newfrank5")]), NEW_frank[,c("franken_a_4a")], NEW_frank[,c("newfrank5")]) NEW_frank$newfrank6<-ifelse(is.na(NEW_frank[,c("franken_4b")]), NEW_frank[,c("nsrs9")], NEW_frank[,c("franken_4b")]) NEW_frank$newfrank6<-ifelse(is.na(NEW_frank[,c("newfrank6")]), NEW_frank[,c("franken_a_4b")], NEW_frank[,c("newfrank6")]) NEW_frank$newfrank7<-ifelse(is.na(NEW_frank[,c("franken_5")]), NEW_frank[,c("franken_a_5")], NEW_frank[,c("franken_5")]) NEW_frank$newfrank8<-ifelse(is.na(NEW_frank[,c("franken_5a")]), NEW_frank[,c("nsrs10b")], NEW_frank[,c("franken_5a")]) NEW_frank$newfrank8<-ifelse(is.na(NEW_frank[,c("newfrank8")]), NEW_frank[,c("franken_a_5a")], NEW_frank[,c("newfrank8")]) NEW_frank$newfrank9<-ifelse(is.na(NEW_frank[,c("franken_6")]), NEW_frank[,c("nsrs5")], NEW_frank[,c("franken_6")]) NEW_frank$newfrank9<-ifelse(is.na(NEW_frank[,c("newfrank9")]), NEW_frank[,c("franken_a_6")], NEW_frank[,c("newfrank9")]) NEW_frank$newfrank10<-ifelse(is.na(NEW_frank[,c("franken_7")]), NEW_frank[,c("franken_a_7")], NEW_frank[,c("franken_7")]) NEW_frank$newfrank11<-ifelse(is.na(NEW_frank[,c("franken_8")]), NEW_frank[,c("franken_a_8")], NEW_frank[,c("franken_8")]) NEW_frank$newfrank12<-ifelse(is.na(NEW_frank[,c("franken_9")]), NEW_frank[,c("nsrs6")], NEW_frank[,c("franken_9")]) NEW_frank$newfrank12<-ifelse(is.na(NEW_frank[,c("newfrank12")]), NEW_frank[,c("franken_a_9")], NEW_frank[,c("newfrank12")]) NEW_frank$newfrank13<-ifelse(is.na(NEW_frank[,c("franken_10")]), NEW_frank[,c("franken_a_10")], NEW_frank[,c("franken_10")]) NEW_frank$newfrank14<-ifelse(is.na(NEW_frank[,c("franken_11")]), NEW_frank[,c("franken_a_11")], NEW_frank[,c("franken_11")]) NEW_frank$newfrank15<-ifelse(is.na(NEW_frank[,c("franken_11a")]), NEW_frank[,c("nsrs15")], NEW_frank[,c("franken_11a")]) NEW_frank$newfrank15<-ifelse(is.na(NEW_frank[,c("newfrank15")]), NEW_frank[,c("franken_a_10a")], NEW_frank[,c("newfrank15")]) NEW_frank$newfrank16<-ifelse(is.na(NEW_frank[,c("franken_11b")]), NEW_frank[,c("nsrs16b")], NEW_frank[,c("franken_11b")]) NEW_frank$newfrank16<-ifelse(is.na(NEW_frank[,c("newfrank16")]), NEW_frank[,c("franken_a_11a")], NEW_frank[,c("newfrank16")]) NEW_frank$newfrank17<-ifelse(is.na(NEW_frank[,c("franken_11c")]), NEW_frank[,c("nsrs11")], NEW_frank[,c("franken_11c")]) NEW_frank$newfrank17<-ifelse(is.na(NEW_frank[,c("newfrank17")]), NEW_frank[,c("franken_a_12")], NEW_frank[,c("newfrank17")]) NEW_frank$newfrank18<-ifelse(is.na(NEW_frank[,c("franken_11d")]), NEW_frank[,c("nsrs12")], NEW_frank[,c("franken_11d")]) NEW_frank$newfrank18<-ifelse(is.na(NEW_frank[,c("newfrank18")]), NEW_frank[,c("franken_a_12a")], NEW_frank[,c("newfrank18")]) NEW_frank$newfrank19<-ifelse(is.na(NEW_frank[,c("franken_11e")]), NEW_frank[,c("nsrs13b")], NEW_frank[,c("franken_11e")]) NEW_frank$newfrank19<-ifelse(is.na(NEW_frank[,c("newfrank19")]), NEW_frank[,c("franken_a_13")], NEW_frank[,c("newfrank19")]) NEW_frank$newfrank20<-ifelse(is.na(NEW_frank[,c("franken_11f")]), NEW_frank[,c("nsrs14")], NEW_frank[,c("franken_11f")]) NEW_frank$newfrank20<-ifelse(is.na(NEW_frank[,c("newfrank20")]), NEW_frank[,c("franken_a_10b")], NEW_frank[,c("newfrank20")]) NEW_frank$newfrank21<-ifelse(is.na(NEW_frank[,c("franken_12")]), NEW_frank[,c("nsrs17")], NEW_frank[,c("franken_12")]) NEW_frank$newfrank21<-ifelse(is.na(NEW_frank[,c("newfrank21")]), NEW_frank[,c("franken_a_14")], NEW_frank[,c("newfrank21")]) NEW_frank$newfrank22<-ifelse(is.na(NEW_frank[,c("franken_13")]), NEW_frank[,c("nsrs18")], NEW_frank[,c("franken_13")]) NEW_frank$newfrank22<-ifelse(is.na(NEW_frank[,c("newfrank22")]), NEW_frank[,c("franken_a_15")], NEW_frank[,c("newfrank22")]) NEW_frank$newfrank23<-ifelse(is.na(NEW_frank[,c("franken_14")]), NEW_frank[,c("franken_a_16")], NEW_frank[,c("franken_14")]) NEW_frank$newfrank24<-ifelse(is.na(NEW_frank[,c("franken_15")]), NEW_frank[,c("nsrs19")], NEW_frank[,c("franken_15")]) NEW_frank$newfrank24<-ifelse(is.na(NEW_frank[,c("newfrank24")]), NEW_frank[,c("franken_a_17")], NEW_frank[,c("newfrank24")]) NEW_frank$newfrank25<-ifelse(is.na(NEW_frank[,c("franken_16")]), NEW_frank[,c("nsrs22")], NEW_frank[,c("franken_16")]) NEW_frank$newfrank25<-ifelse(is.na(NEW_frank[,c("newfrank25")]), NEW_frank[,c("franken_a_18")], NEW_frank[,c("newfrank25")]) NEW_frank2<-NEW_frank[,c('participant_id','bblid','frankenversion','newfrank1','newfrank2','newfrank3','newfrank4','newfrank5','newfrank6','newfrank7','newfrank8','newfrank9','newfrank10','newfrank11','newfrank12', 'newfrank13','newfrank14','newfrank15','newfrank16','newfrank17','newfrank18','newfrank19','newfrank20','newfrank21','newfrank22','newfrank23','newfrank24','newfrank25'), drop=FALSE] currentDate<-Sys.Date() write.csv(NEW_frank2, paste("/import/monstrum/Users/adaldal/Newfrank_", currentDate, ".csv", sep=''), row.names=F)

25b4ebf9b83aa20bf62ac50a2384e119a9385831

cafff9b400a5f31e92176ec294517cdc43a8dc86

/Zero models.R

70d2b420693903d973f532703b97306d9fddb366

[]

no_license

camillemellin/TrueAbsencesInSDMs

df279c65efc6d5da41130e7180920154d9a79018

7af2357c28b028ef0fdc7870d6e698aee43069b8

refs/heads/main

2023-01-23T08:59:35.589144

2020-11-30T03:48:40

304,510,467

1

0

null

UTF-8

R

false

39,343

r

Zero models.R

################################################################## # ZERO MODELS - CM 09/07/19 # ################################################################## # Load libraries ------------ rm(list = ls()) library(dplyr) library(stringr) library(RLSPrivate) library(psych) library(vegan) library(goeveg) library(RColorBrewer) library(tidyr) library(boral) library(corrplot) library(broom) library(visreg) library(metafor) library(mgcv) library(ggplot2) library(gridExtra) library(pscl) library(sp) library(ks) library(gstat) library(PBSmapping) library(sf) library(cowplot) library(psych) library(spatialkernel) library(lme4) library(pROC) library(caret) library(ecospat) scale2 <- function(x){ (x-mean(x, na.rm = T))/sd(x, na.rm = T) } # Load and filter data -------------- setwd("~/Dropbox/My documents/Projects/UTAS/xxx MS Statistical report/modelling") load('Zero models.RData') aus <- importShapefile("~/Dropbox/My documents/Projects/UTAS/NESP/SoE/250K_coastline", readDBF=FALSE) aus2 <- aus %>% dplyr::select(group=PID, POS=POS,long=X,lat=Y) aus.sf <- st_read("~/Dropbox/My documents/Projects/UTAS/NESP/SoE/250K_coastline.shp") data(fdat) names(fdat)[5] <- "SPECIES_NAME" load('rls-sites-plectropomus.rda') GBR_fish_data <- read.table("GBR_fish_data.csv", header = TRUE, quote = "'", sep = ",") GBR_site_data <- read.table("GBR_site_data.csv", header = TRUE, quote = "'", sep = ",") GBR_fish_data$SurveyDate <- as.Date(GBR_fish_data$SurveyDate, "%d/%m/%y") # Build metadata and list of sites surveyed both pre and post bleaching GBR_metadata <- GBR_fish_data %>% filter(Pre.or.post.bleach != "during" & include == "Y" & Method != 0) %>% group_by(SiteCode, Site.name, SiteLat, SiteLong, Reef, SurveyID, SurveyDate, Pre.or.post.bleach, Year) %>% summarize() GBR_metadata_pre <- GBR_metadata %>% filter(Pre.or.post.bleach == "Pre") GBR_metadata_post <- GBR_metadata %>% filter(Pre.or.post.bleach == "Post") GBR_site.ls <- GBR_metadata %>% filter(SiteCode %in% GBR_metadata_pre$SiteCode & SiteCode %in% GBR_metadata_post$SiteCode) %>% group_by(SiteCode, SiteLat, SiteLong) %>% summarize() # Compute pre vs. post species-site matrices based on different methods for zero insertion ------ # 1- Ignore zeros # 2- Add zeros for all species in the data matrix when not recorded at a site. # 3- Add zeros for species recorded at each individual site on at least one occasion (this is what I have done for the population trend analysis). # 4- Add zeros for species that occur within a convex hull that encompasses the site (i.e. extent of occurrence), where the kernel is calculated using all data. # 5- Add zeros for species that occur within a convex hull that encompasses the site, where the kernel is calculated for that year/time slice only. This scenario takes into account changes in distributional ranges through time. GBR_fish_data <- GBR_fish_data %>% filter(CLASS %in% fish_classes() & Method == 1) GBR_fish_data$SPECIES_NAME <- factor(GBR_fish_data$SPECIES_NAME) #1- Ignore zeros GBR_fish_site_1 <- GBR_fish_data %>% filter(SiteCode %in% GBR_site.ls$SiteCode & Pre.or.post.bleach != "during" & include == "Y" & Method != 0) %>% group_by(SiteCode, Site.name, SiteLat, SiteLong, SurveyID, Year, Pre.or.post.bleach, SPECIES_NAME) %>% summarise(N = sum(N, na.rm=T)) %>% group_by(SiteCode, Site.name, SiteLat, SiteLong, Pre.or.post.bleach, Year, SPECIES_NAME) %>% summarise(N = mean(N, na.rm=T)) %>% group_by(SiteCode, Site.name, SiteLat, SiteLong, Pre.or.post.bleach, SPECIES_NAME) %>% summarise(N.1 = mean(N, na.rm=T)) #2- Add zeros everywhere when not recorded at a site GBR_fish_site_2 <- GBR_fish_data %>% filter(SiteCode %in% GBR_site.ls$SiteCode & Pre.or.post.bleach != "during" & include == "Y" & Method != 0) %>% group_by(SiteCode, Site.name, SiteLat, SiteLong, SurveyID, Year, Pre.or.post.bleach, SPECIES_NAME) %>% summarise(N = sum(N, na.rm=T)) %>% ungroup() %>% complete(nesting(SiteCode, Site.name, SiteLat, SiteLong, SurveyID, Year, Pre.or.post.bleach), SPECIES_NAME, fill = list(N = 0)) %>% group_by(SiteCode, Site.name, SiteLat, SiteLong, Pre.or.post.bleach, Year, SPECIES_NAME) %>% summarise(N = mean(N, na.rm=T)) %>% group_by(SiteCode, Site.name, SiteLat, SiteLong, Pre.or.post.bleach, SPECIES_NAME) %>% summarise(Year = mean(Year), N.2 = mean(N, na.rm=T)) #3- Add zeros for species recorded at each individual site on at least one occasion, and at sites whithin their vicinity (i.e. within 1-degree radius) # Or use KDE? SiteSpecies.ls <- GBR_fish_site_1 %>% group_by(SiteCode, SiteLat, SiteLong, SPECIES_NAME) %>% summarise() GBR_spp.ls <- names(table(SiteSpecies.ls$SPECIES_NAME)[table(SiteSpecies.ls$SPECIES_NAME)>0]) SiteSpecies.ls.rd <- SiteSpecies.ls SiteSpecies.ls.rd$SiteLong <- round(SiteSpecies.ls.rd$SiteLong) SiteSpecies.ls.rd$SiteLat <- round(SiteSpecies.ls.rd$SiteLat) GBR_site.ls.rd <- GBR_site.ls GBR_site.ls.rd$SiteLong <- round(GBR_site.ls.rd$SiteLong) GBR_site.ls.rd$SiteLat <- round(GBR_site.ls.rd$SiteLat) SiteSpecies.ls.rd <- SiteSpecies.ls.rd %>% left_join(GBR_site.ls.rd, by = c("SiteLong", "SiteLat")) GBR_fish_site_3 <- subset(GBR_fish_site_2, paste(SiteCode, SPECIES_NAME, sep = "_") %in% with(SiteSpecies.ls.rd, paste(SiteCode.y, SPECIES_NAME, sep = "_"))) #GBR_fish_site_3 <- subset(GBR_fish_site_2, paste(SiteCode, SPECIES_NAME, sep = "_") %in% with(SiteSpecies.ls, paste(SiteCode, SPECIES_NAME, sep = "_"))) names(GBR_fish_site_3)[ncol(GBR_fish_site_3)] <- "N.3" # 4- Add zeros for species that occur within a convex hull that encompasses the site (i.e. extent of occurrence), where the kernel is calculated using all data. SiteSpecies.ls.4 <- data.frame(SPECIES_NAME = as.character(NA), SiteCode = NA) GBR_spp_range.area <- data.frame(SPECIES_NAME = GBR_spp.ls, range.area = NA) for (i in 1:length(GBR_spp.ls)) { hull.data <- subset(SiteSpecies.ls, SPECIES_NAME == GBR_spp.ls[i], select = c(SiteLong, SiteLat)) hull <- chull(hull.data) hull <- c(hull, hull[1]) GBR_spp_range.area$range.area[i] <- areapoly(as.matrix(hull.data[hull,]))$area GBR_site.in.hull <- point.in.polygon(GBR_site.ls$SiteLong, GBR_site.ls$SiteLat, hull.data$SiteLong[hull], hull.data$SiteLat[hull]) SiteSpecies.ls.4 <- rbind(SiteSpecies.ls.4, data.frame(SPECIES_NAME = GBR_spp.ls[i], SiteCode = GBR_site.ls$SiteCode[GBR_site.in.hull %in% c(1,3)])) } SiteSpecies.ls.4 <- SiteSpecies.ls.4[-1,] # Check convex hulls and inside/outside sites plot(hull.data) lines(hull.data[hull,]) polygon(hull.data[hull,], col = "lightgrey") points(GBR_site.ls$SiteLong, GBR_site.ls$SiteLat, col = "blue", pch = 19) points(hull.data, pch = 19, col = "green") points(GBR_site.ls$SiteLong[GBR_site.in.hull %in% c(1,3)], GBR_site.ls$SiteLat[GBR_site.in.hull %in% c(1,3)], col = "red", pch = 19) GBR_fish_site_4 <- subset(GBR_fish_site_2, paste(SiteCode, SPECIES_NAME, sep = "_") %in% with(SiteSpecies.ls.4, paste(SiteCode, SPECIES_NAME, sep = "_"))) names(GBR_fish_site_4)[ncol(GBR_fish_site_4)] <- "N.4" # 5- Add zeros for species that occur within a convex hull that encompasses the site, where the kernel is calculated for that year/time slice only. This scenario takes into account changes in distributional ranges through time. SiteSpecies.ls.pre.post <- GBR_fish_site_1 %>% group_by(SiteCode, SiteLat, SiteLong, Pre.or.post.bleach, SPECIES_NAME) %>% summarise() SiteSpecies.ls.5 <- data.frame(SPECIES_NAME = as.character(NA), SiteCode = NA, Pre.or.post.bleach = NA) for (i in 1:length(GBR_spp.ls)) { pre.hull.data <- subset(SiteSpecies.ls.pre.post, SPECIES_NAME == GBR_spp.ls[i] & Pre.or.post.bleach == "Pre", select = c(SiteLong, SiteLat)) pre.hull <- chull(pre.hull.data) pre.hull <- c(pre.hull, pre.hull[1]) GBR_site.in.pre.hull <- point.in.polygon(GBR_site.ls$SiteLong, GBR_site.ls$SiteLat, pre.hull.data$SiteLong[pre.hull], pre.hull.data$SiteLat[pre.hull]) if(length(GBR_site.ls$SiteCode[GBR_site.in.pre.hull %in% c(1,3)]) > 0) { SiteSpecies.ls.5 <- rbind(SiteSpecies.ls.5, data.frame(SPECIES_NAME = GBR_spp.ls[i], SiteCode = GBR_site.ls$SiteCode[GBR_site.in.pre.hull %in% c(1,3)], Pre.or.post.bleach = "Pre")) } post.hull.data <- subset(SiteSpecies.ls.pre.post, SPECIES_NAME == GBR_spp.ls[i] & Pre.or.post.bleach == "Post", select = c(SiteLong, SiteLat)) post.hull <- chull(post.hull.data) post.hull <- c(post.hull, post.hull[1]) GBR_site.in.post.hull <- point.in.polygon(GBR_site.ls$SiteLong, GBR_site.ls$SiteLat, post.hull.data$SiteLong[post.hull], post.hull.data$SiteLat[post.hull]) if(length(GBR_site.ls$SiteCode[GBR_site.in.post.hull %in% c(1,3)]) > 0) { SiteSpecies.ls.5 <- rbind(SiteSpecies.ls.5, data.frame(SPECIES_NAME = GBR_spp.ls[i], SiteCode = GBR_site.ls$SiteCode[GBR_site.in.post.hull %in% c(1,3)], Pre.or.post.bleach = "Post")) } } SiteSpecies.ls.5 <- SiteSpecies.ls.5[-1,] # Check convex hulls and inside/outside sites par(mfcol = c(2,1)) plot(pre.hull.data) lines(pre.hull.data[pre.hull,]) polygon(pre.hull.data[pre.hull,], col = "lightgrey") points(GBR_site.ls$SiteLong, GBR_site.ls$SiteLat, col = "blue", pch = 19) points(pre.hull.data, pch = 19, col = "green") points(GBR_site.ls$SiteLong[GBR_site.in.pre.hull %in% c(1,3)], GBR_site.ls$SiteLat[GBR_site.in.pre.hull %in% c(1,3)], col = "red", pch = 19) plot(post.hull.data) lines(post.hull.data[post.hull,]) polygon(post.hull.data[post.hull,], col = "lightgrey") points(GBR_site.ls$SiteLong, GBR_site.ls$SiteLat, col = "blue", pch = 19) points(post.hull.data, pch = 19, col = "green") points(GBR_site.ls$SiteLong[GBR_site.in.post.hull %in% c(1,3)], GBR_site.ls$SiteLat[GBR_site.in.post.hull %in% c(1,3)], col = "red", pch = 19) GBR_fish_site_5 <- subset(GBR_fish_site_2, paste(SiteCode, Pre.or.post.bleach, SPECIES_NAME, sep = "_") %in% with(SiteSpecies.ls.5, paste(SiteCode, Pre.or.post.bleach, SPECIES_NAME, sep = "_"))) names(GBR_fish_site_5)[ncol(GBR_fish_site_5)] <- "N.5" # Build single table with 5 abundance estimates, one for each method GBR_fish_site_all <- GBR_fish_site_2 %>% left_join(GBR_fish_site_1, by = c("SiteCode","Site.name","SiteLat","SiteLong","SPECIES_NAME","Pre.or.post.bleach")) %>% left_join(GBR_fish_site_3, by = c("SiteCode","Site.name","SiteLat","SiteLong","SPECIES_NAME","Pre.or.post.bleach")) %>% left_join(GBR_fish_site_4, by = c("SiteCode","Site.name","SiteLat","SiteLong","SPECIES_NAME","Pre.or.post.bleach")) %>% left_join(GBR_fish_site_5, by = c("SiteCode","Site.name","SiteLat","SiteLong","SPECIES_NAME","Pre.or.post.bleach")) %>% dplyr::select("SiteCode","Site.name","SiteLat","SiteLong","Year", "Pre.or.post.bleach","SPECIES_NAME","N.1","N.2","N.3","N.4","N.5") GBR_fish_site_P <- GBR_fish_site_all GBR_fish_site_P[,7:11][GBR_fish_site_P[,7:11] > 0] <- 1 names(GBR_fish_site_P)[7:11] <- c("P.1", "P.2", "P.3", "P.4", "P.5") # Illustrate the method with Ctenochaetus cyanocheilus ----- plot.data.pre <- GBR_fish_site_all %>% filter(SPECIES_NAME == "Ctenochaetus cyanocheilus" & Pre.or.post.bleach == "Pre") plot.data.post <- GBR_fish_site_all %>% filter(SPECIES_NAME == "Ctenochaetus cyanocheilus" & Pre.or.post.bleach == "Post") plot.data.pre[,7:11][plot.data.pre[,7:11] > 0] <- 1 plot.data.post[,7:11][plot.data.post[,7:11] > 0] <- 1 map.1_pre <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + # xlab(expression(paste(Longitude^o, ~'E'))) + # ylab(expression(paste(Latitude^o, ~'S'))) + geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.pre[plot.data.pre$N.1 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.pre[plot.data.pre$N.1 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank()) + theme_void() map.1_post <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + # xlab(expression(paste(Longitude^o, ~'E'))) + # ylab(expression(paste(Latitude^o, ~'S'))) + geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.post[plot.data.post$N.1 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.post[plot.data.post$N.1 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank())+ theme_void() map.2_pre <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + xlab(expression(paste(Longitude^o, ~'E'))) + ylab(expression(paste(Latitude^o, ~'S'))) + geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.pre[plot.data.pre$N.2 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.pre[plot.data.pre$N.2 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank())+ theme_void() map.2_post <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + xlab(expression(paste(Longitude^o, ~'E'))) + ylab(expression(paste(Latitude^o, ~'S'))) + geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.post[plot.data.post$N.2 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.post[plot.data.post$N.2 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank())+ theme_void() map.3_pre <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + xlab(expression(paste(Longitude^o, ~'E'))) + ylab(expression(paste(Latitude^o, ~'S'))) + geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.pre[plot.data.pre$N.3 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.pre[plot.data.pre$N.3 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank())+ theme_void() map.3_post <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + xlab(expression(paste(Longitude^o, ~'E'))) + ylab(expression(paste(Latitude^o, ~'S'))) + geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.post[plot.data.post$N.3 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.post[plot.data.post$N.3 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank())+ theme_void() map.4_pre <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + xlab(expression(paste(Longitude^o, ~'E'))) + ylab(expression(paste(Latitude^o, ~'S'))) + geom_polygon(data = hull.data[hull,], aes(SiteLong, SiteLat), fill = "lightblue", alpha = .8)+ geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.pre[plot.data.pre$N.4 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.pre[plot.data.pre$N.4 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank())+ theme_void() map.4_post <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + xlab(expression(paste(Longitude^o, ~'E'))) + ylab(expression(paste(Latitude^o, ~'S'))) + geom_polygon(data = hull.data[hull,], aes(SiteLong, SiteLat), fill = "lightblue", alpha = .8)+ geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.post[plot.data.post$N.4 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.post[plot.data.post$N.4 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank())+ theme_void() map.5_pre <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + xlab(expression(paste(Longitude^o, ~'E'))) + ylab(expression(paste(Latitude^o, ~'S'))) + geom_polygon(data = pre.hull.data[pre.hull,], aes(SiteLong, SiteLat), fill = "lightblue", alpha = .8)+ geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.pre[plot.data.pre$N.5 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.pre[plot.data.pre$N.5 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank())+ theme_void() map.5_post <- ggplot() + geom_polygon(data=aus2, aes(long, lat, group=group), fill="lightgray", color="darkgray") + coord_map(xlim=c(143,156), ylim=c(-22,-10)) + xlab(expression(paste(Longitude^o, ~'E'))) + ylab(expression(paste(Latitude^o, ~'S'))) + geom_polygon(data = post.hull.data[post.hull,], aes(SiteLong, SiteLat), fill = "lightblue", alpha = .8)+ geom_point(data=GBR_site.ls, aes(SiteLong, SiteLat), size=1, shape=19, colour="dimgrey") + geom_point(data = plot.data.post[plot.data.post$N.5 == 0,], aes(SiteLong, SiteLat), size = 2, shape = 19, colour="cornflowerblue")+ geom_point(data = plot.data.post[plot.data.post$N.5 == 1,], aes(SiteLong, SiteLat), size = 3, shape = 19, colour="tomato")+ theme(text=element_text(size=12, family="Calibri"), plot.margin = unit(c(.1,.1,.1,.1), "cm"), axis.title = element_blank())+ theme_void() map.pre.post <- plot_grid(plotlist = list(map.1_pre, map.1_post, map.2_pre, map.2_post, map.3_pre, map.3_post, map.4_pre, map.4_post, map.5_pre, map.5_post), ncol=2, nrow=5) # Compare species frequency distribution -------- spp_freq_pre <- GBR_fish_site_P %>% filter(Pre.or.post.bleach == "Pre") %>% group_by(SPECIES_NAME) %>% summarize(F.2 = sum(P.2, na.rm = T)/n_distinct(SiteCode[P.2 %in% c(0,1)]), F.3 = sum(P.3, na.rm = T)/n_distinct(SiteCode[P.3 %in% c(0,1)]), F.4 = sum(P.4, na.rm = T)/n_distinct(SiteCode[P.4 %in% c(0,1)]), F.5 = sum(P.5, na.rm = T)/n_distinct(SiteCode[P.5 %in% c(0,1)])) %>% filter(F.5 < 1) d.2.pre <- with(spp_freq_pre, density(F.2, na.rm = T)) d.3.pre <- with(spp_freq_pre, density(F.3, na.rm = T)) d.4.pre <- with(spp_freq_pre, density(F.4, na.rm = T)) d.5.pre <- with(spp_freq_pre, density(F.5, na.rm = T)) spp_freq_post <- GBR_fish_site_P %>% filter(Pre.or.post.bleach == "Post") %>% group_by(SPECIES_NAME) %>% summarize(F.2 = sum(P.2, na.rm = T)/n_distinct(SiteCode[P.2 %in% c(0,1)]), F.3 = sum(P.3, na.rm = T)/n_distinct(SiteCode[P.3 %in% c(0,1)]), F.4 = sum(P.4, na.rm = T)/n_distinct(SiteCode[P.4 %in% c(0,1)]), F.5 = sum(P.5, na.rm = T)/n_distinct(SiteCode[P.5 %in% c(0,1)])) %>% filter(F.5 < 1) d.2.post <- with(spp_freq_post, density(F.2, na.rm = T)) d.3.post <- with(spp_freq_post, density(F.3, na.rm = T)) d.4.post <- with(spp_freq_post, density(F.4, na.rm = T)) d.5.post <- with(spp_freq_post, density(F.5, na.rm = T)) plot(d.2.pre, type = "l", ylim = c(0,5)) lines(d.2.post, lty = 2) lines(d.3.pre, col = "green") lines(d.3.post, col = "green", lty = 2) lines(d.4.pre, col = "red") lines(d.4.post, col = "red", lty = 2) lines(d.5.pre, col = "orange") lines(d.5.post, col = "orange", lty = 2) pairs.panels(spp_freq_pre[,-1]) pairs.panels(spp_freq_post[,-1]) spp_freq_pre <- spp_freq_pre %>% left_join(GBR_spp_range.area) spp_freq_post <- spp_freq_post %>% left_join(GBR_spp_range.area) density.plot <- ggplot() + geom_density(data = spp_freq_pre, aes(F.2, color = "M.2", lty = "Before"), size = 1)+ geom_density(data = spp_freq_post, aes(F.2, color = "M.2", lty = "After"), size = 1)+ geom_density(data = spp_freq_pre, aes(F.3, color = "M.3", lty = "Before"), size = 1)+ geom_density(data = spp_freq_post, aes(F.3, color = "M.3", lty = "After"), size = 1)+ geom_density(data = spp_freq_pre, aes(F.4, color = "M.4", lty = "Before"), size = 1)+ geom_density(data = spp_freq_post, aes(F.4, color = "M.4", lty = "After"), size = 1)+ geom_density(data = spp_freq_pre, aes(F.5, color = "M.5", lty = "Before"), size = 1)+ geom_density(data = spp_freq_post, aes(F.5, color = "M.5", lty = "After"), size = 1)+ xlab("Species frequency")+ theme(legend.position = 'right') + scale_color_manual(values = c('#172A3A','#006166','#508991',"#09BC8A"), labels = c("M.2", "M.3", "M.4", "M.5"), name = "Method")+ scale_linetype_manual(values = factor(c(1,3)), labels = c("Before", "After"), name = "Bleaching") biplot <- ggplot() + geom_point(data = spp_freq_pre, aes(x = F.2, y = F.4, color = range.area))+ xlab("Species frequency (M1)") + ylab("Species frequency (M4)") fig2 <- plot_grid(density.plot, biplot, ncol=1, nrow=2) fig2 # Build corresponding Site matrix --------------- # Process SST data from coral trout dataset dat_plectro2 <- dat_plectro %>% dplyr::select(SiteCode, PrePost, sst_mean, sst_year, sst_anom, Live_hard_coral) %>% group_by(SiteCode, PrePost) %>% summarize_all(mean) %>% data.frame() dat_plectro2$PrePost <- factor(dat_plectro2$PrePost, levels = c("Before", "After", "Pre", "Post")) dat_plectro2$PrePost <- ifelse(dat_plectro2$PrePost == "Before", "Pre", "Post") dat_plectro2$PrePost <- factor(dat_plectro2$PrePost) all.dat <- GBR_fish_site_P %>% left_join(dat_plectro2, by = c('SiteCode', 'Pre.or.post.bleach' = 'PrePost')) # Logistic GLMM for each species --------- # See link on Cohen's kappa: https://stats.stackexchange.com/questions/82162/cohens-kappa-in-plain-english # Calculate spp.frequency (only include spp occurring at 15 sites or more, i.e. 155 species) spp.frequency <- GBR_fish_site_P %>% group_by(SPECIES_NAME) %>% summarize(Nocc = sum(P.1, na.rm = T), Nocc.pre = sum(P.1[Pre.or.post.bleach == "Pre"], na.rm = T), Nocc.post = sum(P.1[Pre.or.post.bleach == "Post"], na.rm = T)) %>% filter(Nocc > 20 & Nocc.pre > 10 & Nocc.post > 10) %>% left_join(GBR_spp_range.area) all.dat.sub <- all.dat %>% filter(SPECIES_NAME %in% spp.frequency$SPECIES_NAME) all.dat.sub <- data.frame(all.dat.sub) for (i in 12:15) all.dat.sub[,i] <- scale2(all.dat.sub[,i]) M2.coef <- M3.coef <- M4.coef <- M5.coef <- M2.pval <- M3.pval <- M4.pval <- M5.pval <- data.frame(SPECIES_NAME = spp.frequency$SPECIES_NAME, "(Intercept)"=NA, "Live_hard_coral"=NA, "sst_mean"=NA, "sst_anom"=NA, "sst_mean:sst_anom"=NA) AUC <- accuracy <- kappa <- boyce <- tss <- data.frame(SPECIES_NAME = spp.frequency$SPECIES_NAME, "m2"=NA, "m3"=NA, "m4"=NA, "m5"=NA) for (i in 1:length(spp.frequency$SPECIES_NAME)) { sp.dat <- subset(all.dat.sub, SPECIES_NAME == spp.frequency$SPECIES_NAME[i]) m2 <- glmer(P.2 ~ Live_hard_coral + sst_mean*sst_anom + (1|Pre.or.post.bleach), data = sp.dat, family = binomial) m3 <- glmer(P.3 ~ Live_hard_coral + sst_mean*sst_anom + (1|Pre.or.post.bleach), data = sp.dat, family = binomial) m4 <- glmer(P.4 ~ Live_hard_coral + sst_mean*sst_anom + (1|Pre.or.post.bleach), data = sp.dat, family = binomial) m5 <- glmer(P.5 ~ Live_hard_coral + sst_mean*sst_anom + (1|Pre.or.post.bleach), data = sp.dat, family = binomial) M2.coef[i,-1] <- summary(m2)$coefficients[,1] M3.coef[i,-1] <- summary(m3)$coefficients[,1] M4.coef[i,-1] <- summary(m4)$coefficients[,1] M5.coef[i,-1] <- summary(m5)$coefficients[,1] M2.pval[i,-1] <- summary(m2)$coefficients[,4] M3.pval[i,-1] <- summary(m3)$coefficients[,4] M4.pval[i,-1] <- summary(m4)$coefficients[,4] M5.pval[i,-1] <- summary(m5)$coefficients[,4] pred.m2 <- predict(m2, newdata = sp.dat, type = "response") pred.m3 <- predict(m3, newdata = sp.dat, type = "response") pred.m4 <- predict(m4, newdata = sp.dat, type = "response") pred.m5 <- predict(m5, newdata = sp.dat, type = "response") # Remove NA values from predictions and observations for calculating accuracy metrics obs.m2 <- sp.dat$P.2[!is.na(pred.m2)] obs.m3 <- sp.dat$P.3[!is.na(pred.m3)] obs.m4 <- sp.dat$P.4[!is.na(pred.m4)] obs.m5 <- sp.dat$P.5[!is.na(pred.m5)] pred.m2 <- na.omit(pred.m2) pred.m3 <- na.omit(pred.m3) pred.m4 <- na.omit(pred.m4) pred.m5 <- na.omit(pred.m5) AUC$m2[i] <- auc(roc(obs.m2, pred.m2, quiet = T)) AUC$m3[i] <- auc(roc(obs.m3, pred.m3, quiet = T)) AUC$m4[i] <- auc(roc(obs.m4, pred.m4, quiet = T)) AUC$m5[i] <- auc(roc(obs.m5, pred.m5, quiet = T)) boyce$m2[i] <- ecospat.boyce(as.numeric(pred.m2), as.numeric(pred.m2[which(obs.m2 == 1)]), nclass=0, window.w="default", res=100, PEplot = F)$Spearman.cor boyce$m3[i] <- ecospat.boyce(as.numeric(pred.m3), as.numeric(pred.m3[which(obs.m3 == 1)]), nclass=0, window.w="default", res=100, PEplot = F)$Spearman.cor boyce$m4[i] <- ecospat.boyce(as.numeric(pred.m4), as.numeric(pred.m4[which(obs.m4 == 1)]), nclass=0, window.w="default", res=100, PEplot = F)$Spearman.cor boyce$m5[i] <- ecospat.boyce(as.numeric(pred.m5), as.numeric(pred.m5[which(obs.m5 == 1)]), nclass=0, window.w="default", res=100, PEplot = F)$Spearman.cor p2 <- as.numeric(pred.m2>0.5) accuracy$m2[i] <- mean(p2==obs.m2, na.rm = T) c2 <- confusionMatrix(factor(p2), factor(obs.m2)) kappa$m2[i] <- c2$overall[2] tss$m2[i] <- c2$byClass["Sensitivity"] + c2$byClass["Specificity"] - 1 p3 <- as.numeric(pred.m3>0.5) accuracy$m3[i] <- mean(p3==obs.m3, na.rm = T) c3 <- confusionMatrix(factor(p3), factor(obs.m3)) kappa$m3[i] <- c3$overall[3] tss$m3[i] <- c3$byClass["Sensitivity"] + c3$byClass["Specificity"] - 1 p4 <- as.numeric(pred.m4>0.5) accuracy$m4[i] <- mean(p4==obs.m4, na.rm = T) c4 <- confusionMatrix(factor(p4), factor(obs.m4)) kappa$m4[i] <- c4$overall[4] tss$m4[i] <- c4$byClass["Sensitivity"] + c4$byClass["Specificity"] - 1 p5 <- as.numeric(pred.m5>0.5) accuracy$m5[i] <- mean(p5==obs.m5, na.rm = T) c5 <- confusionMatrix(factor(p5), factor(obs.m5)) kappa$m5[i] <- c5$overall[5] tss$m5[i] <- c5$byClass["Sensitivity"] + c5$byClass["Specificity"] - 1 print(i) } M2.coef[,-1][M2.pval[,-1] > 0.05] <- NA M3.coef[,-1][M3.pval[,-1] > 0.05] <- NA M4.coef[,-1][M4.pval[,-1] > 0.05] <- NA M5.coef[,-1][M5.pval[,-1] > 0.05] <- NA # Logistic GLMM for each species: fit on PRE data, predict POST data --------- kappa.prepost <- tss.prepost <- data.frame(SPECIES_NAME = spp.frequency$SPECIES_NAME, "m2"=NA, "m3"=NA, "m4"=NA, "m5"=NA) for (i in 1:length(spp.frequency$SPECIES_NAME)) { sp.dat <- subset(all.dat.sub, SPECIES_NAME == spp.frequency$SPECIES_NAME[i]) m2 <- glm(P.2 ~ Live_hard_coral + sst_mean*sst_anom, data = sp.dat[sp.dat$Pre.or.post.bleach == "Pre",], family = binomial) m3 <- glm(P.3 ~ Live_hard_coral + sst_mean*sst_anom, data = sp.dat[sp.dat$Pre.or.post.bleach == "Pre",], family = binomial) m4 <- glm(P.4 ~ Live_hard_coral + sst_mean*sst_anom, data = sp.dat[sp.dat$Pre.or.post.bleach == "Pre",], family = binomial) m5 <- glm(P.5 ~ Live_hard_coral + sst_mean*sst_anom, data = sp.dat[sp.dat$Pre.or.post.bleach == "Pre",], family = binomial) pred.m2 <- predict(m2, newdata = sp.dat[sp.dat$Pre.or.post.bleach == "Post",], type = "response") pred.m3 <- predict(m3, newdata = sp.dat[sp.dat$Pre.or.post.bleach == "Post",], type = "response") pred.m4 <- predict(m4, newdata = sp.dat[sp.dat$Pre.or.post.bleach == "Post",], type = "response") pred.m5 <- predict(m5, newdata = sp.dat[sp.dat$Pre.or.post.bleach == "Post",], type = "response") # Remove NA values from predictions and observations for calculating accuracy metrics obs.m2 <- sp.dat$P.2[sp.dat$Pre.or.post.bleach == "Pre"][!is.na(pred.m2)] obs.m3 <- sp.dat$P.3[sp.dat$Pre.or.post.bleach == "Pre"][!is.na(pred.m3)] obs.m4 <- sp.dat$P.4[sp.dat$Pre.or.post.bleach == "Pre"][!is.na(pred.m4)] obs.m5 <- sp.dat$P.5[sp.dat$Pre.or.post.bleach == "Pre"][!is.na(pred.m5)] pred.m2 <- na.omit(pred.m2) pred.m3 <- na.omit(pred.m3) pred.m4 <- na.omit(pred.m4) pred.m5 <- na.omit(pred.m5) p2 <- as.numeric(pred.m2>0.5) c2 <- confusionMatrix(factor(p2), factor(obs.m2)) kappa.prepost$m2[i] <- c2$overall[2] tss.prepost$m2[i] <- c2$byClass["Sensitivity"] + c2$byClass["Specificity"] - 1 p3 <- as.numeric(pred.m3>0.5) c3 <- confusionMatrix(factor(p3), factor(obs.m3)) kappa.prepost$m3[i] <- c3$overall[3] tss.prepost$m3[i] <- c3$byClass["Sensitivity"] + c3$byClass["Specificity"] - 1 p4 <- as.numeric(pred.m4>0.5) c4 <- confusionMatrix(factor(p4), factor(obs.m4)) kappa.prepost$m4[i] <- c4$overall[4] tss.prepost$m4[i] <- c4$byClass["Sensitivity"] + c4$byClass["Specificity"] - 1 p5 <- as.numeric(pred.m5>0.5) c5 <- confusionMatrix(factor(p5), factor(obs.m5)) kappa.prepost$m5[i] <- c5$overall[5] tss.prepost$m5[i] <- c5$byClass["Sensitivity"] + c5$byClass["Specificity"] - 1 print(i) } kappa.prepost[kappa.prepost < 0] <- 0 tss.prepost[tss.prepost < 0] <- 0 # Heat matrices of model coefficients (Fig. 3) --------------- # Heat matrix for %Coral coral.all <- data.frame(SPECIES_NAME = M2.coef$SPECIES_NAME, M2 = M2.coef$Live_hard_coral, M3 = M3.coef$Live_hard_coral, M4 = M4.coef$Live_hard_coral, M5 = M5.coef$Live_hard_coral) coral.all <- subset(coral.all, !(is.na(M3) & is.na(M4) & is.na(M5))) coral.all[is.na(coral.all)] <- 0 coral.all <- coral.all[order(coral.all$M5, coral.all$M4, coral.all$M3, decreasing = F),] coral.all <- data.matrix(coral.all[,-1]) coral.all[coral.all > quantile(coral.all, .9)] <- quantile(coral.all, .9) coral.all[coral.all < quantile(coral.all, .1)] <- quantile(coral.all, .1) corrplot(t(coral.all), col = rev(brewer.pal(11,"RdBu")), method = "color", is.corr= FALSE, cl.pos = "n", addgrid.col = "lightgrey", tl.pos = "n") cor(coral.all) coral.all.recl <- coral.all coral.all.recl[coral.all.recl < 0] <- 1 coral.all.recl[coral.all.recl > 0] <- 1 # False positives # M3 = 10% length(coral.all.recl[,3][coral.all.recl[,3] == 1 & coral.all.recl[,4] == 0]) * 100/dim(coral.all.recl)[1] # M2 = 15.8% length(coral.all.recl[,2][coral.all.recl[,2] == 1 & coral.all.recl[,4] == 0]) * 100/dim(coral.all.recl)[1] # M1 = 13.3% length(coral.all.recl[,1][coral.all.recl[,1] == 1 & coral.all.recl[,4] == 0]) * 100/dim(coral.all.recl)[1] # False negatives # M3 = 10% length(coral.all.recl[,3][coral.all.recl[,3] == 0 & coral.all.recl[,4] == 1]) * 100/dim(coral.all.recl)[1] # M2 = 15.8% length(coral.all.recl[,2][coral.all.recl[,2] == 0 & coral.all.recl[,4] == 1]) * 100/dim(coral.all.recl)[1] # M1 = 13.3% length(coral.all.recl[,1][coral.all.recl[,1] == 0 & coral.all.recl[,4] == 1]) * 100/dim(coral.all.recl)[1] # Heat matrix for %sst sst.all <- data.frame(SPECIES_NAME = M2.coef$SPECIES_NAME, M2 = M2.coef$sst_anom, M3 = M3.coef$sst_anom, M4 = M4.coef$sst_anom, M5 = M5.coef$sst_anom) sst.all <- subset(sst.all, !(is.na(M3) & is.na(M4) & is.na(M5))) sst.all[is.na(sst.all)] <- 0 sst.all <- sst.all[order(sst.all$M5, sst.all$M4, sst.all$M3, decreasing = F),] sst.all <- data.matrix(sst.all[,-1]) sst.all[sst.all > quantile(sst.all, .9)] <- quantile(sst.all, .9) sst.all[sst.all < quantile(sst.all, .1)] <- quantile(sst.all, .1) corrplot(t(sst.all), col = rev(brewer.pal(11,"RdBu")), method = "color", is.corr= FALSE, cl.pos = "n", addgrid.col = "lightgrey", tl.pos = "n") cor(sst.all) sst.all.recl <- sst.all sst.all.recl[sst.all.recl < 0] <- 1 sst.all.recl[sst.all.recl > 0] <- 1 # False positives # M3 = 10% length(sst.all.recl[,3][sst.all.recl[,3] == 1 & sst.all.recl[,4] == 0]) * 100/dim(sst.all.recl)[1] # M2 = 15.8% length(sst.all.recl[,2][sst.all.recl[,2] == 1 & sst.all.recl[,4] == 0]) * 100/dim(sst.all.recl)[1] # M1 = 13.3% length(sst.all.recl[,1][sst.all.recl[,1] == 1 & sst.all.recl[,4] == 0]) * 100/dim(sst.all.recl)[1] # False negatives # M3 = 10% length(sst.all.recl[,3][sst.all.recl[,3] == 0 & sst.all.recl[,4] == 1]) * 100/dim(sst.all.recl)[1] # M2 = 15.8% length(sst.all.recl[,2][sst.all.recl[,2] == 0 & sst.all.recl[,4] == 1]) * 100/dim(sst.all.recl)[1] # M1 = 13.3% length(sst.all.recl[,1][sst.all.recl[,1] == 0 & sst.all.recl[,4] == 1]) * 100/dim(sst.all.recl)[1] # Identify range shifting species ------- Species_lat_ranges <- GBR_fish_site_P %>% #filter(SPECIES_NAME %in% spp.frequency$SPECIES_NAME) %>% group_by(SPECIES_NAME) %>% summarize(min.lat.pre = min(SiteLat[P.1 == 1 & Pre.or.post.bleach == "Pre"], na.rm = T), max.lat.pre = max(SiteLat[P.1 == 1 & Pre.or.post.bleach == "Pre"], na.rm = T), min.lat.post = min(SiteLat[P.1 == 1 & Pre.or.post.bleach == "Post"], na.rm = T), max.lat.post = max(SiteLat[P.1 == 1 & Pre.or.post.bleach == "Post"], na.rm = T)) %>% filter(is.finite(min.lat.pre)) Species_lat_ranges$lat.ext.pre <- with(Species_lat_ranges, max.lat.pre - min.lat.pre) Species_lat_ranges$lat.ext.post <- with(Species_lat_ranges, max.lat.post - min.lat.post) Species_lat_ranges$lat.ext.change <- with(Species_lat_ranges, lat.ext.post - lat.ext.pre) length(Species_lat_ranges$lat.ext.change[Species_lat_ranges$lat.ext.change < -1]) #25 species with range contraction (9.9%) length(Species_lat_ranges$lat.ext.change[Species_lat_ranges$lat.ext.change > 1]) #29 species with range extension (11.5%) Species_lat_ranges$lat.mp.pre <- with(Species_lat_ranges, (min.lat.pre + max.lat.pre)/2) Species_lat_ranges$lat.mp.post <- with(Species_lat_ranges,(min.lat.post + max.lat.post)/2) Species_lat_ranges$lat.mp.change <- with(Species_lat_ranges, lat.mp.post - lat.mp.pre) length(Species_lat_ranges$lat.mp.change[Species_lat_ranges$lat.mp.change < -1]) #22 species with southern midpoint displacement length(Species_lat_ranges$lat.mp.change[Species_lat_ranges$lat.mp.change > 1]) #8 species with northern midpoint displacement Species_lat_ranges$range.cont <- ifelse(Species_lat_ranges$lat.ext.change < -1, 1, 0) Species_lat_ranges$range.ext <- ifelse(Species_lat_ranges$lat.ext.change > 1, 1, 0) Species_lat_ranges$range.displ <- ifelse(Species_lat_ranges$lat.mp.change > 1 | Species_lat_ranges$lat.mp.change < -1, 1, 0) Species_lat_ranges$range.change <- ifelse(Species_lat_ranges$range.ext == 1 | Species_lat_ranges$range.cont == 1 | Species_lat_ranges$range.displ == 1, 1, 0) length(Species_lat_ranges$range.ext[Species_lat_ranges$range.ext ==1])/length(Species_lat_ranges$range.ext) length(Species_lat_ranges$range.cont[Species_lat_ranges$range.cont ==1])/length(Species_lat_ranges$range.ext) length(Species_lat_ranges$range.displ[Species_lat_ranges$range.displ ==1])/length(Species_lat_ranges$range.ext) length(Species_lat_ranges$range.change[Species_lat_ranges$range.change ==1])/length(Species_lat_ranges$range.ext) # Distribution of TSS and Kappa for all species vs. range-shifting species -------- stats <- rbind(data.frame(method = "M1", SPECIES_NAME = tss$SPECIES_NAME, tss = tss$m2, kappa = kappa$m2), data.frame(method = "M2", SPECIES_NAME = tss$SPECIES_NAME, tss = tss$m3, kappa = kappa$m3), data.frame(method = "M3", SPECIES_NAME = tss$SPECIES_NAME, tss = tss$m4, kappa = kappa$m4), data.frame(method = "M4", SPECIES_NAME = tss$SPECIES_NAME, tss = tss$m5, kappa = kappa$m5)) stats$tss[stats$tss < 0] <- 0.001 stats$kappa[stats$kappa < 0] <- 0.001 g.kappa <- ggplot(stats, aes(factor(method), kappa)) + #geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) geom_boxplot(notch = T) + xlab("Method") + ylab("Kappa") + ylim(0,1) + ggtitle("All species") g.kappa.range.change <- ggplot(stats[stats$SPECIES_NAME %in% Species_lat_ranges$SPECIES_NAME[Species_lat_ranges$range.change ==1],], aes(factor(method), kappa)) + #geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) geom_boxplot(notch = T) + xlab("Method") + ylab("Kappa") + ylim(0,1) + ggtitle("Range-shifting species") g.tss <- ggplot(stats, aes(factor(method), tss)) + #geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) geom_boxplot(notch = T) + xlab("Method") + ylab("TSS") + ylim(0,.6) g.tss.range.change <- ggplot(stats[stats$SPECIES_NAME %in% Species_lat_ranges$SPECIES_NAME[Species_lat_ranges$range.change ==1],], aes(factor(method), tss)) + #geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) geom_boxplot(notch = T) + xlab("Method") + ylab("TSS") + ylim(0,.6) plot_grid(g.kappa, g.kappa.range.change, g.tss, g.tss.range.change, nrow = 2, ncol = 2) g.biplot <- ggplot() + geom_point(data = tss, aes(x = m2, y = m5), color = "dimgrey")+ xlab("TSS (M.2)") + ylab("TSS (M.4)") + geom_abline(aes(slope = 1, intercept = 0), color = "dimgrey") + geom_density_2d(data = tss, aes(x = m2, y = m5), color = "blue")+ geom_density_2d(data = tss[tss$SPECIES_NAME %in% Species_lat_ranges$SPECIES_NAME[Species_lat_ranges$range.change ==1],], aes(x = m2, y = m5), color = "green")+ #stat_density_2d(data = tss, aes(x = m2, y = m5, fill = after_stat(level)), alpha = .5, geom = "polygon")+ coord_cartesian(xlim = c(0,1), ylim = c(0,1)) ggplot() + geom_point(data = tss, aes(x = m2, y = m4, color = log10(spp.frequency$Nocc)))+ xlab("TSS (M.2)") + ylab("TSS (M.4)")

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1615831663-test.R

testlist <- list(doy = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), latitude = numeric(0), temp = c(8.5728629954997e-312, 1.5688525430436e+82, 8.96970809549085e-158, -1.3258495253834e-113, 2.79620616433656e-119, -6.80033518839696e+41, 2.68298522855314e-211, 1444042902784.06, 6.68889884134308e+51, -4.05003163986346e-308, -3.52601820453991e+43, -1.49815227045093e+197, -2.61599376411615e+76, 4.82650578930004e+76, -1.94295658750812e-157, 5.21464652810224e-302, -7.5949865592493e+118, 1.07054513907543e-219, -4.2324579017604e+95, -1.3199888952305e+101, -9.4183172679602e+144)) result <- do.call(meteor:::ET0_ThornthwaiteWilmott,testlist) str(result)

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ModellerEngineFunctions.R

############################################################################# ####################### ModellerEngien Licensecing & Login ################## ############################################################################# # sidebar Modelling Technique functions Am_function <- function(){ menuItem("AutoModeller",tabName = "ols_am",icon = icon("user"), menuItem("Bucketing",tabName = "am_Bucketing",icon = icon("user")), menuItem("Acquire", tabName = "Acquire", icon = icon("upload"), menuItem("Transformation",tabName = "directCsvUpload", icon = icon("database")) ), menuItem("Analyse", tabName = "Analyse", icon = icon("th"), menuItem("Data review",tabName = "AM_Data_Review", icon = icon("tasks")), menuItem("Results",tabName = "AM_Result", icon = icon("line-chart")), menuItem("Filter Results",tabName = "AM_FilterResult", icon = icon("filter", lib = "glyphicon"),badgeLabel = "new",badgeColor = "red"), menuItem("Top Models",tabName = "AM_TopResult", icon = icon("line-chart"),badgeLabel = "new",badgeColor = "red") ) ) } MMM_function <- function(){ menuItem("MMM",tabName = "ols_manual",icon = icon("user"), menuItem("AF Scope",tabName = "olsm_csvUp", icon = icon("database")), menuItem("Model Scope",tabName = "olsm_modelScope",icon = icon("filter", lib = "glyphicon")), menuItem("Model Manager", tabName = "olsm_modelManager", icon = icon("tasks")), menuItem("Results",tabName = "olsm_results", icon = icon("line-chart")) ) } DLM_function <- function(){ menuItem("DLM Project",tabName = "karma_dlm",icon = icon("user"), #### Side Bars #### menuItem("DLM_Transformtion",tabName = "dlm_trans",icon = icon("user"), #### Side Bars #### shinydashboard:: menuItem(tabName = "dlm_Trans_data", text = "Data Transformation", icon = icon("tasks"), selected = TRUE) ), menuItem("DLM_Modelling",tabName = "dlm_Modelling",icon = icon("user"), shinydashboard:: menuItem(tabName = "data", text = "Data", icon = icon("home"), selected = TRUE), shinydashboard:: menuItem(tabName = "explore", text = "Explore", icon = icon("cogs")), shinydashboard:: menuItem(tabName = "par", text = "DLM Parameters", icon = icon("edit")), shinydashboard:: menuItem(tabName = "dlm", text = "DLM", icon = icon("download")), shinydashboard:: menuItem(tabName = "info", text = "Info & Documentation", icon = icon("file"))) ) } EDA_function <- function(){ menuItem("EDA",tabName = "MEEDAProcess",icon = icon("user"), menuItem("Data Viewer",tabName = "kDataViewer",icon = icon("bar-chart-o"),badgeLabel = "WIP",badgeColor = "red"), menuItem("DataAnalysis",tabName = "kEDA",icon = icon("line-chart"), menuItem("Summary",tabName = "kSummary",icon = icon("upload"),badgeLabel = "WIP",badgeColor = "red"), menuItem("Univariate",tabName = "kUnivariate", icon = icon("columns"),badgeLabel = "WIP",badgeColor = "red"), menuItem("Bivariate",tabName = "kBivariate", icon = icon("columns"),badgeLabel = "WIP",badgeColor = "red"), menuItem("Multivariate",tabName = "kMultivariate", icon = icon("columns"),badgeLabel = "WIP",badgeColor = "red")), menuItem("Clustering ",tabName = "kClustering",icon = icon("columns"),badgeLabel = "WIP",badgeColor = "red") ) } VOF_function <- function(){ menuItem("Variable Console",tabName = "VOF",icon = icon("book"), menuItem("Variable Console",tabName = "kVOF",icon = icon("book")), menuItem("Console Help",tabName = "Console_help",icon = icon("upload"))) } # build sidebar based on UserAccess to display UserAccessSidebarConditions <- function(userAccess, loggedUser){ userAccess <- MEUsersLoginDetail[which(MEUsersLoginDetail$username == loggedUser),] tmpSidebarMMM <- NULL if(userAccess[["MMM"]] == TRUE){ tmpSidebarMMM <- paste0(tmpSidebarMMM, menuItem("MMM Project",tabName = "MEProjectTab",icon = icon("user"), menuItem("New Project",tabName = "newProjectTab",icon = icon("database")), menuItem("Saved Project",tabName = "oldProjectTab",icon = icon("database")), menuItem("Import/Export Project",tabName = "sharedProjectTab",icon = icon("database")), EDA_function(),VOF_function(),MMM_function() ) ) } tmpSidebarDLM <- NULL if(userAccess[["DLM"]] == TRUE){ tmpSidebarDLM <- paste0(tmpSidebarDLM,DLM_function()) } tmpSidebarZippyGeo <- NULL if(userAccess[["GeoMMM"]] == TRUE){ tmpSidebarZippyGeo <- paste0(tmpSidebarZippyGeo,menuItem("GeoMMM Project",tabName = "karmaZippyGeo",icon = icon("user"))) } tmpSidebarBayes <- NULL if(userAccess[["Bayesian"]] == TRUE){ #tmpSidebarBayes <- paste0(tmpSidebarBayes,menuItem("Bayesian Project",tabName = "karmaBayes",icon = icon("user"))) } tmpSidebarML_Workbench <- NULL if(userAccess[["ML_Workbench"]] == TRUE){ tmpSidebarML_Workbench <- paste0(tmpSidebarML_Workbench, menuItem("ML WorkBench Project",tabName = "karmaML_WorkBench",icon = icon("user"), menuItem("Data Load", tabName = "ML_FileUpload", icon = icon("upload")), menuItem("Parameter Setup", tabName = "ML_VariableShortlist", icon = icon("table")), menuItem("Models Comparison", tabName = "ML_ModelCompare", icon = icon("filter", lib = "glyphicon")) #menuItem("Linear Regression", tabName = "ML_LinearRegression", icon = icon("cogs")), #menuItem("Gradient Boosting",tabName = "ML_GBM",icon = icon("cogs")), #menuItem("XGBoost",tabName = "ML_XGBoost",icon = icon("cogs")), #menuItem("ANN Modelling", tabName = "ML_H2oANN", icon = icon("cogs")), #menuItem("Bayesian",tabName = "ML_Bayes",icon = icon("cogs")) #menuItem("Bayesian Belief",tabName = "ML_BayesBelief",icon = icon("cogs")) #menuItem("Hierarchical Bayesian",tabName = "ML_HBayes",icon = icon("cogs")), #menuItem("IMR",tabName = "ML_IMR",icon = icon("cogs")) ) ) } tmpSidebarBeta <- NULL if(userAccess[["Beta"]] == TRUE){ tmpSidebarBeta <- paste0(tmpSidebarBeta,menuItem("Beta",tabName = "karmaBeta",icon = icon("user"),Am_function())) } return(HTML(paste0(HTML(tmpSidebarMMM), HTML(tmpSidebarDLM), #HTML(tmpSidebarZippyGeo), HTML(tmpSidebarBayes), HTML(tmpSidebarML_Workbench), HTML(tmpSidebarBeta)) ) ) } # Modelling options dispaly based on UserAccess after AF upload. UserModellingButtonDisplay <- function(userAccess){ tmpModellingButton <- NULL if(userAccess[["MMM"]] == TRUE){ tmpModellingButton <- paste0(tmpModellingButton,column(3,actionButton("olsmProceed","MMM",style="color: #ffffff ; background-color: #455a64 ; border-color: #455a64;margin: 2px"))) } if(userAccess[["Beta"]] == TRUE){ tmpModellingButton <- paste0(tmpModellingButton,column(4,actionButton("am_Proceed","AutoModeller",style="color: #ffffff ; background-color: #455a64; border-color: #455a64;margin: 2px"))) } return(HTML(tmpModellingButton)) } ############################################################################# ####################### AutoModeller functions ########################## ############################################################################# # Root Mean Square Error function rmse <- function(x,y){ return(sqrt(sum((x-y)^2)/(length(x)))) } # Mean Absolute Percentage Error function mape <- function(x,y){ return(sum(abs(sapply(1:length(x), function(i){(x[i]-y[i])/x[i]})))*(100/length(x))/100) } applyModellingPeriod <- function(df,startDate,endDate){ dfDateSubset <- subset(df, df$period >= ymd(startDate) & df$period <= ymd(endDate)) return(dfDateSubset) } #Changine alpha function for automodeller getAlpha <- function(df_lagged,df,alpha,beta,df_variable){ df_laggedDT <- as.data.table(df_lagged) dfDT <- as.data.table(df) alpha <- alpha[complete.cases(alpha)] for(name in names(alpha)){ if(max(dfDT[[name]],na.rm = T) != 0){ set(x = df_laggedDT,j = name,value = (as.numeric(unname(beta[name]))/(10^10))^((as.numeric(unname(alpha[name]))^((as.numeric(df_laggedDT[[name]])/max(as.numeric(dfDT[[df_variable]]),na.rm = T))*100)))) #Refresnce formula #(1/(10^10))^(as.numeric(unname(alpha[name]))^((df_lagged[,name]/max(df_lagged[,name]))*100)) } else{ df_laggedDT[[name]] <- 0 } } df <- as.data.frame(df_laggedDT) return(df) } # lag for complete tranformation applyDfLag <- function(df,lag){ df_Lag <- as.data.table(df) for(name in names(lag)){ if(!is.na(lag[name])) { df_Lag[,(name):=shift(df_Lag[[name]],as.numeric(unname(lag[name])),fill = 0,type = "lag")] } } df_lagged <- as.data.frame(df_Lag) return(df_lagged) } #power getPower <- function(df,powerRange){ dfPowerDt <- as.data.table(df) powerSeries <- powerRange[complete.cases(powerRange)] for(name in names(powerSeries)) { set(dfPowerDt,j = name,value=dfPowerDt[[name]]^as.numeric(unname(powerSeries[name]))) } df <- as.list.data.frame(dfPowerDt) return(df) } #Decay getDecay <- function(df,decay){ df_DecayDT <- as.data.table(df) decay <- decay[complete.cases(decay)] calcDecay <- function(col,decay){ for(i in 1:length(col)){ if(i ==1){ col[i] <- as.numeric(col[i]) } else if(!is.na(col[i - 1])){ col[i] <- as.numeric(col[i])+ as.numeric(col[i - 1]*(1-decay)) } } return(col) } for(name in names(decay)) { set(df_DecayDT,j = name,value=calcDecay(df_DecayDT[[name]],as.numeric(unname(decay[name])))) } df <- as.data.frame(df_DecayDT) return(df) } # transform the data as per bucket CreateAllTransformations <- function(df, trnsList, amInputBucketList){ colNames <- names(df) transVar <- as.character(unlist(amInputBucketList[names(amInputBucketList) %in% trnsList$selectedTransBucket])) dfToNonTrans <- df[,-which(names(df) %in% c("Period",transVar))] dfToTrans <- data.frame(df[,which(names(df) %in% transVar)],stringsAsFactors = F) names(dfToTrans) <- names(df)[which(names(df) %in% transVar)] if(length(dfToTrans)==1){ dfToTrans[,1] <- as.numeric(dfToTrans[,1]) }else{ dfToTrans <- as.data.frame(apply(dfToTrans,2,as.numeric)) } transformedDf <- list() for (name in names(dfToNonTrans)) { transformedDf[[name]][[name]] <- as.numeric(dfToNonTrans[,name]) } for(name in names(dfToTrans)){ # Transforming the data as per the bucket selection. transformedDf[[name]]<- list() lagTrans <- as.data.frame(replicate(as.numeric(as.character(dfToTrans[,name])), n = (trnsList$getLagMax-trnsList$getLagMin)+1),stringsAsFactors = F) lagSeries <- as.numeric(trnsList$getLagMin:trnsList$getLagMax) names(lagSeries) <- paste0(name,"_L",trnsList$getLagMin:trnsList$getLagMax) names(lagTrans) <- names(lagSeries) lagTrans <- applyDfLag(df = lagTrans,lag = lagSeries) if(trnsList$decaySelection == "Alpha Decay"){ alphaDecayTransList <- alphaDecayTrans(dfToTrans,lagTrans,name,trnsList) transformedDf[[name]] <- append(transformedDf[[name]],values = alphaDecayTransList) }else if(trnsList$decaySelection == "Power Decay"){ powerDecayTransList <- powerDecayTrans(dfToTrans,lagTrans,name,trnsList) transformedDf[[name]] <- append(transformedDf[[name]],values = powerDecayTransList) }else if(trnsList$decaySelection == "Decay Power"){ decayPowerTransList <- decayPowerTrans(dfToTrans,lagTrans,name,trnsList) transformedDf[[name]] <- append(transformedDf[[name]],values = decayPowerTransList) }else if(trnsList$decaySelection == "Decay Alpha"){ decayAlphaTransList <- decayAlphaTrans(dfToTrans,lagTrans,name,trnsList) transformedDf[[name]] <- append(transformedDf[[name]],values = decayAlphaTransList) } } return(transformedDf) } #capturing Alpha Decay data alphaDecayTrans <- function(dfToTrans,lagTrans,name,trnsList){ alphaTransformedList <- list() for(lagName in names(lagTrans)){ AlphaSeries <- as.numeric(seq(from=trnsList$getAlphaMin,to=trnsList$getAlphaMax,by=trnsList$getAlphaSteps)) lagTransAlpha <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])),n = length(AlphaSeries)),stringsAsFactors = F) names(AlphaSeries) <- paste0(lagName,"_A",seq(from=trnsList$getAlphaMin,to=trnsList$getAlphaMax,by=trnsList$getAlphaSteps)) names(lagTransAlpha) <- names(AlphaSeries) betaSeries <- rep(1,times=length(AlphaSeries)) names(betaSeries) <- names(AlphaSeries) df <- as.data.frame(dfToTrans[,name]) colnames(df) <- name lagTransAlpha <- getAlpha(df_lagged = lagTransAlpha,df = df,alpha = AlphaSeries,beta = betaSeries,df_variable = name) for(alphaName in names(lagTransAlpha)){ decaySeries <- as.numeric(seq(from=trnsList$getDecayMin,to=trnsList$getDecayMax,by=trnsList$getDecaySteps)) lagTransAlphaDecay <- as.data.frame(replicate(as.numeric(as.character(lagTransAlpha[,alphaName])),n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(alphaName,"_D",seq(from=trnsList$getDecayMin,to=trnsList$getDecayMax,by=trnsList$getDecaySteps)) names(lagTransAlphaDecay) <- names(decaySeries) lagTransAlphaDecay <- getDecay(lagTransAlphaDecay,decaySeries) alphaTransformedList <- c(alphaTransformedList,lagTransAlphaDecay) } } return(alphaTransformedList) } #capturing Power Decay data powerDecayTrans <- function(dfToTrans,lagTrans,name,trnsList){ powerTransformedList <- list() for(lagName in names(lagTrans)){ powerSeries <- as.numeric(seq(from=trnsList$getPowerMin,to=trnsList$getPowerMax,by=trnsList$getPowerSteps)) lagTransPower <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])), n = length(powerSeries)),stringsAsFactors = F) names(powerSeries) <- paste0(lagName,"_P",seq(from=trnsList$getPowerMin,to=trnsList$getPowerMax,by=trnsList$getPowerSteps)) names(lagTransPower) <- names(powerSeries) lagTransPower <- as.data.frame.list(getPower(lagTransPower,powerSeries)) for(powerName in names(lagTransPower)){ decaySeries <- as.numeric(seq(from=trnsList$getDecayMin,to=trnsList$getDecayMax,by=trnsList$getDecaySteps)) lagTransPowerDecay <- as.data.frame(replicate(as.numeric(as.character(lagTransPower[,powerName])),n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(powerName,"_D",seq(from=trnsList$getDecayMin,to=trnsList$getDecayMax,by=trnsList$getDecaySteps)) names(lagTransPowerDecay) <- names(decaySeries) lagTransPowerDecay <- getDecay(lagTransPowerDecay,decaySeries) powerTransformedList <- c(powerTransformedList,lagTransPowerDecay) } } return(powerTransformedList) } #capturing Decay Power data decayPowerTrans <- function(dfToTrans,lagTrans,name,trnsList){ powerTransformedList <- list() for(lagName in names(lagTrans)){ decaySeries <- as.numeric(seq(from=trnsList$getDecayMin,to=trnsList$getDecayMax,by=trnsList$getDecaySteps)) lagTransDecay <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])), n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(lagName,"_D",seq(from=trnsList$getDecayMin,to=trnsList$getDecayMax,by=trnsList$getDecaySteps)) names(lagTransDecay) <- names(decaySeries) lagTransDecay <- getDecay(lagTransDecay,decaySeries) for(powerName in names(lagTransDecay)){ powerSeries <- as.numeric(seq(from=trnsList$getPowerMin,to=trnsList$getPowerMax,by=trnsList$getPowerSteps)) lagTransDecayPower <- as.data.frame(replicate(as.numeric(as.character(lagTransDecay[,powerName])), n = length(powerSeries)),stringsAsFactors = F) names(powerSeries) <- paste0(powerName,"_P",seq(from=trnsList$getPowerMin,to=trnsList$getPowerMax,by=trnsList$getPowerSteps)) names(lagTransDecayPower) <- names(powerSeries) lagTransDecayPower <- getPower(lagTransDecayPower,powerSeries) powerTransformedList <- c(powerTransformedList,lagTransDecayPower) } } return(powerTransformedList) } #capturing Decay Alpha data decayAlphaTrans <- function(dfToTrans,lagTrans,name,trnsList){ alphaTransformedList <- list() for(lagName in names(lagTrans)){ decaySeries <- as.numeric(seq(from=trnsList$getDecayMin,to=trnsList$getDecayMax,by=trnsList$getDecaySteps)) lagTransDecay <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])), n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(lagName,"_D",seq(from=trnsList$getDecayMin,to=trnsList$getDecayMax,by=trnsList$getDecaySteps)) names(lagTransDecay) <- names(decaySeries) lagTransDecay <- getDecay(lagTransDecay,decaySeries) for(alphaName in names(lagTransDecay)){ AlphaSeries <- as.numeric(seq(from=trnsList$getAlphaMin,to=trnsList$getAlphaMax,by=trnsList$getAlphaSteps)) lagTransDecayAlpha <- as.data.frame(replicate(as.numeric(as.character(lagTransDecay[,alphaName])),n = length(AlphaSeries)),stringsAsFactors = F) names(AlphaSeries) <- paste0(alphaName,"_A",seq(from=trnsList$getAlphaMin,to=trnsList$getAlphaMax,by=trnsList$getAlphaSteps)) names(lagTransDecayAlpha) <- names(AlphaSeries) betaSeries <- rep(1,times=length(AlphaSeries)) names(betaSeries) <- names(AlphaSeries) df <- as.data.frame(dfToTrans[,name]) colnames(df) <- alphaName lagTransDecayAlpha <- getAlpha(df_lagged = lagTransDecayAlpha,df = df,alpha = AlphaSeries,beta = betaSeries,df_variable = alphaName) alphaTransformedList <- c(alphaTransformedList,lagTransDecayAlpha) } } return(alphaTransformedList) } getTransformedVariables <- function(amTransDataList,parametersDf){ transformedVariablesIndexDf <- parametersDf[which(parametersDf$Transformation != "Linear"),] transformedVariablesIndexDf$Bucket <- as.character(transformedVariablesIndexDf$Bucket) transformedVariablesDf <- data.frame() variables <- NULL for(i in 1:nrow(transformedVariablesIndexDf)){ if(transformedVariablesIndexDf$Transformation[i] == "Decay Power"){ variables <- sapply(paste0(transformedVariablesIndexDf$VariableName[i],"_L",as.numeric(as.character(transformedVariablesIndexDf$LagMin[i])):as.numeric(as.character(transformedVariablesIndexDf$LagMax[i]))),FUN = function(x)sapply(paste0(x,"_D",paste0(seq(from=as.numeric(as.character(transformedVariablesIndexDf$DecayMin[i])),to=as.numeric(as.character(transformedVariablesIndexDf$DecayMax[i])),by=as.numeric(as.character(transformedVariablesIndexDf$DecaySteps[i]))))),FUN = function(y)paste0(y,"_P",paste0(seq(from=as.numeric(as.character(transformedVariablesIndexDf$PowerMin[i])),to=as.numeric(as.character(transformedVariablesIndexDf$PowerMax[i])),by=as.numeric(as.character(transformedVariablesIndexDf$PowerSteps[i]))))),simplify = T),simplify = T) } else if (transformedVariablesIndexDf$Transformation[i] == "Alpha Decay"){ variables <- sapply(paste0(transformedVariablesIndexDf$VariableName[i],"_L",as.numeric(as.character(transformedVariablesIndexDf$LagMin[i])):as.numeric(as.character(transformedVariablesIndexDf$LagMax[i]))),FUN = function(x)sapply(paste0(x,"_A",paste0(seq(from=as.numeric(as.character(transformedVariablesIndexDf$AlphaMin[i])),to=as.numeric(as.character(transformedVariablesIndexDf$AlphaMax[i])),by=as.numeric(as.character(transformedVariablesIndexDf$AlphaSteps[i]))))),FUN = function(y)paste0(y,"_D",paste0(seq(from=as.numeric(as.character(transformedVariablesIndexDf$DecayMin[i])),to=as.numeric(as.character(transformedVariablesIndexDf$DecayMax[i])),by=as.numeric(as.character(transformedVariablesIndexDf$DecaySteps[i]))))),simplify = T),simplify = T) } else if(transformedVariablesIndexDf$Transformation[i] == "Power Decay"){ variables <- sapply(paste0(transformedVariablesIndexDf$VariableName[i],"_L",as.numeric(as.character(transformedVariablesIndexDf$LagMin[i])):as.numeric(as.character(transformedVariablesIndexDf$LagMax[i]))),FUN = function(x)sapply(paste0(x,"_P",paste0(seq(from=as.numeric(as.character(transformedVariablesIndexDf$PowerMin[i])),to=as.numeric(as.character(transformedVariablesIndexDf$PowerMax[i])),by=as.numeric(as.character(transformedVariablesIndexDf$PowerSteps[i]))))),FUN = function(y)paste0(y,"_D",paste0(seq(from=as.numeric(as.character(transformedVariablesIndexDf$DecayMin[i])),to=as.numeric(as.character(transformedVariablesIndexDf$DecayMax[i])),by=as.numeric(as.character(transformedVariablesIndexDf$DecaySteps[i]))))),simplify = T),simplify = T) } else if (transformedVariablesIndexDf$Transformation[i] == "Decay Alpha"){ variables <- sapply(paste0(transformedVariablesIndexDf$VariableName[i],"_L",as.numeric(as.character(transformedVariablesIndexDf$LagMin[i])):as.numeric(as.character(transformedVariablesIndexDf$LagMax[i]))),FUN = function(x)sapply(paste0(x,"_D",paste0(seq(from=as.numeric(as.character(transformedVariablesIndexDf$DecayMin[i])),to=as.numeric(as.character(transformedVariablesIndexDf$DecayMax[i])),by=as.numeric(as.character(transformedVariablesIndexDf$DecaySteps[i]))))),FUN = function(y)paste0(y,"_A",paste0(seq(from=as.numeric(as.character(transformedVariablesIndexDf$AlphaMin[i])),to=as.numeric(as.character(transformedVariablesIndexDf$AlphaMax[i])),by=as.numeric(as.character(transformedVariablesIndexDf$AlphaSteps[i]))))),simplify = T),simplify = T) } tempDf <- as.data.frame.list(amTransDataList[[transformedVariablesIndexDf$Bucket[i]]][[transformedVariablesIndexDf$VariableName[i]]][names(amTransDataList[[transformedVariablesIndexDf$Bucket[i]]][[transformedVariablesIndexDf$VariableName[i]]]) %in% variables]) if(ncol(transformedVariablesDf) ==0 ){ transformedVariablesDf <- tempDf }else{ transformedVariablesDf <- cbind(transformedVariablesDf,tempDf) } } return(transformedVariablesDf) } buildFormulaList <- function(amTransDataList,modelScopeDf,bucketData,parametersDf){ linearNames <- names(amTransDataList)[names(amTransDataList) %in% parametersDf$Bucket[parametersDf$Transformation == "Linear" & parametersDf$ModellingFlag == "Yes" & parametersDf$Bucket != "Dependent"]] nonLinearNames <- names(amTransDataList)[names(amTransDataList) %in% parametersDf$Bucket[parametersDf$Transformation != "Linear" & parametersDf$ModellingFlag == "Yes"]] # calculating the range of combination for each bucket bucketData[,-1] <- apply(bucketData[,-1],2,as.numeric) bucketRangeList <- lapply(bucketData$Bucket, function(x) bucketData$MinVariables[bucketData$Bucket == x]:bucketData$Max[bucketData$Bucket == x]) names(bucketRangeList) <- bucketData$Bucket # generating bucket wise combination for formula building bucketComb <- data.frame(expand.grid(bucketRangeList),stringsAsFactors = F) # getting all variables by bucket. bucketVarList <- list() bucketVarList[["Dependent"]] <- as.character(parametersDf$VariableName[which(parametersDf$Bucket == "Dependent")]) # collecting all linear variables by bucket if(length(linearNames)!= 0){ bucketVarListLr <- lapply(linearNames, function(x) {names(amTransDataList[[x]])[names(amTransDataList[[x]]) %in% parametersDf$VariableName[parametersDf$Bucket==x & parametersDf$ModellingFlag == "Yes"]]}) names(bucketVarListLr) <- linearNames bucketVarList <- append(bucketVarList, bucketVarListLr) } # collecting all nonlinear variables by bucket if(length(nonLinearNames)!= 0){ bucketVarListNLr <- lapply(nonLinearNames, function(x) {names(amTransDataList[[x]])[names(amTransDataList[[x]]) %in% parametersDf$VariableName[parametersDf$Bucket==x & parametersDf$ModellingFlag == "Yes"]]}) names(bucketVarListNLr) <- nonLinearNames for(name in names(bucketVarListNLr)){ tempList <- lapply(bucketVarListNLr[[name]], function(x){names(amTransDataList[[name]][[x]])}) names(tempList) <- bucketVarListNLr[[name]] bucketVarListNLr[[name]] <- tempList } bucketVarList <- append(bucketVarList, bucketVarListNLr) } # generating all possible combination of each bucket by min and max. (by default generating NULL for min 0) bucketVarComb <- list() for(name in names(bucketRangeList)){ # name <- names(bucketRangeList)[1] range <- bucketRangeList[[name]] if(name %in% linearNames){ bucketVarComb[[name]] <- lapply(range, function(x){ if(x!=0){ unlist(combn(bucketVarList[[name]],x,simplify = F,FUN = function(x){paste0(x,collapse = " + ")})) } }) }else{ bucketVarComb[[name]] <- lapply(range, function(x){ if(x!=0){ varCombList <- as.list(as.data.frame(combn(names(bucketVarList[[name]]),x))) apply(varList <- sapply(data.frame(rbindlist(lapply(varCombList, function(x){ data.frame(do.call(expand.grid,bucketVarList[[name]][names(bucketVarList[[name]]) %in% unlist(x)]),stringsAsFactors = F) }))), as.character),1,FUN = function(x){paste(x,collapse = "+")}) } }) } names(bucketVarComb[[name]])<- range } # remove all NULL from nested bucketvVarComb list bucketVarComb <- rlist::list.clean(bucketVarComb,fun = is.null, recursive = T) # baseformula with dependent only baseformula <- paste0(bucketVarList$Dependent," ~ ") # building formula by bucketComb row wise # remove 0 from bucketComb after comnverting into list. formulaList <- NULL for(i in 1:nrow(bucketComb)){ if(length(formulaList) < 200000){ bucket <- as.list(bucketComb[i,]) bucket[bucket == 0] <- NULL if(length(bucket)!= 0){ formulaList <- c(formulaList, paste0(baseformula, apply(expand.grid(lapply(names(bucket), function(x){bucketVarComb[[x]][[as.character(bucket[[x]])]]}),stringsAsFactors = F),1,paste0, collapse = " + "))) } } } return(as.list(formulaList)) } getIterCount <- function(RegDataTemp,amTransDataList, bucketData,parametersDf,startDate, endDate){ modelScopeDf <- getRegDataTable(RegDataTemp,amTransDataList, parametersDf,startDate, endDate) baseFormula <- buildFormulaList(amTransDataList,modelScopeDf,bucketData,parametersDf) return(baseFormula) } getActualVsPredictedDf <- function(modelScopeDf,model){ data <- modelScopeDf actPred <- cbind.data.frame(Period =data[,"period"], Actual = model$model[,1], Predicted = fitted(model), Residual = residuals(model)) return(actPred) } getElasticity <- function(model,parametersDf){ modelScopeMean <- colMeans(model$model) modelScopeMean_12 <- colMeans(tail(model$model,n = 12)) depVar <- parametersDf$VariableName[parametersDf$Bucket=="Dependent"] df <- NULL contribution <- NULL elasticity <- NULL elasticity_12 <- NULL for(name in names(model$coefficients)){ if(name == "(Intercept)"){ contribution[name] <- (model$coefficients[name] *100)/modelScopeMean[depVar] } else { contribution[name] <- model$coefficients[name]*(modelScopeMean[name]/modelScopeMean[depVar])* 100 if(length(parametersDf$Transformation[parametersDf$VariableName == name]) != 0 && parametersDf$Transformation[parametersDf$VariableName == name] == "Linear" || grepl("Dummy",name)){ elasticity[name] <- 5*model$coefficients[name]*(modelScopeMean[name]/modelScopeMean[depVar]) elasticity_12[name] <- 5*model$coefficients[name]*(modelScopeMean_12[name]/modelScopeMean_12[depVar]) } else { decayValue <- as.numeric(gsub("D","",str_extract(name,"D\\d+.\\d+"))) powerValue <- as.numeric(gsub("P","",str_extract(name,"P\\d+.\\d+"))) elasticity[name] <- (((1.01^(powerValue))-1)*100*(model$coefficients[name])*(modelScopeMean[name])/(modelScopeMean[depVar]))*5 elasticity_12[name] <- (((1.01^(powerValue))-1)*100*(model$coefficients[name])*(modelScopeMean_12[name])/(modelScopeMean_12[depVar]))*5 } } } parameterDetails <- cbind(tidy(model),contribution = contribution,VIF=c(0,vif(model)),Elasticity_Modelling_Period = c(0,elasticity),Elasticity_L12_Modelling_Period = c(0,elasticity_12)) return(parameterDetails) } #get data for transformation getDataForTransformation <-function(RegDataTemp,amTransDataList,parametersDf,bucketData,startDate,endDate, baseFormula, rankType){ modelScopeDf <- getRegDataTable(RegDataTemp,amTransDataList, parametersDf,startDate, endDate) # Ashutosh: passing parameterDF to allPossibleRegressions() to get T-stat for each variable given by user system.time(resultList <- allPossibleRegressions(modelScopeDf = modelScopeDf,baseFormula, parametersDf)) result <- resultList$result # if there is no model, then message will display to change parameter. if(length(result) == 1){ resultData <- list() resultData[["Models"]] <- result resultData[["ModelScopeDf"]] <- modelScopeDf resultData[["finalDf"]] <- modelScopeDf resultData[["modelList"]] <- resultList$ModelList return(resultData) }else { rankResultList <- list() rankResultList <- rankModels(data = result,rankType) resultData <- list() if(length(rankResultList) != 1){ ranked_result <- cbind(result, Model_Rank = rankResultList$rank, Model_Score = rankResultList$score) ranked_result$Model_Score <- round(ranked_result$Model_Score,digits = 2) resultData[["Models"]] <- ranked_result resultData[["ModelScopeDf"]] <- modelScopeDf resultData[["finalDf"]] <- modelScopeDf resultData[["modelList"]] <- resultList$ModelList return(resultData) }else{ resultData[["Models"]] <- result resultData[["ModelScopeDf"]] <- modelScopeDf resultData[["finalDf"]] <- modelScopeDf resultData[["modelList"]] <- resultList$ModelList return(resultData) } } } # Function to get data table for linear regression input with specified period range. getRegDataTable <- function(RegDataTemp,amTransDataList,parametersDf,startDate,endDate){ linearVariables <- parametersDf$VariableName[which(parametersDf$Transformation == "Linear")] linearVariablesDf <- as.data.frame(RegDataTemp[-1,][,linearVariables]) names(linearVariablesDf) <- linearVariables if(any(as.character(parametersDf$Transformation)!= "Linear")){ transformedVariableDf <- getTransformedVariables(amTransDataList,parametersDf) finalDf <- cbind(period=lubridate::dmy(RegDataTemp[-1,1]),linearVariablesDf,transformedVariableDf) } else { finalDf <- cbind(period=lubridate::dmy(RegDataTemp[-1,1]),linearVariablesDf) } modelScopeDf <- applyModellingPeriod(finalDf,startDate,endDate) return(modelScopeDf) } getDummyModelResult <- function(model, modelScopeDummyTable, RegDataTemp, modelScopeDf){ if(grepl("Intercept",names(coef(model)))){ candidateModelVar <- names(coef(model))[-1] }else{ candidateModelVar <- names(coef(model)) } candidateModelDf <- as.data.frame(modelScopeDf[which(modelScopeDf$period >= min(modelScopeDummyTable$Period) & modelScopeDf$period <= max(modelScopeDummyTable$Period)),which(names(modelScopeDf) %in% as.character(candidateModelVar))]) names(candidateModelDf) <- as.character(candidateModelVar) dummyDFTable <- as.data.frame(modelScopeDummyTable[,which(names(modelScopeDummyTable) %in% names(which(apply(modelScopeDummyTable[,-1],2,sum)!=0)))]) names(dummyDFTable) <- names(which(apply(modelScopeDummyTable[,-1],2,sum)!=0)) modelScopeDfDepVar <- names(unlist(sapply(RegDataTemp[1,], function(x) which(x == "Dependent")))) modelScopeDfDep <- data.frame(modelScopeDf[which(modelScopeDf$period >= min(modelScopeDummyTable$Period) & modelScopeDf$period <= max(modelScopeDummyTable$Period)), names(modelScopeDf) %in% modelScopeDfDepVar], stringsAsFactors = FALSE) colnames(modelScopeDfDep) <- modelScopeDfDepVar candidateModelScopeDf <- cbind(modelScopeDfDep, candidateModelDf, dummyDFTable) candidateModelScopeDf <- as.data.frame(lapply(candidateModelScopeDf, function(x) as.numeric(as.character(x)))) candidateModelVarList <- list() NonDependent <- names(candidateModelDf) DummyVar <- names(dummyDFTable) NonDependent <- append(NonDependent, DummyVar) candidateModelVarList[["NonDependent"]] <- NonDependent candidateModelVarList[["Dependent"]] <- names(modelScopeDfDep) baseFormula <- as.formula(paste0(candidateModelVarList$Dependent," ~ ", paste0(unlist(candidateModelVarList$NonDependent),collapse = "+"))) modelDummy <- lm(formula = baseFormula, data = candidateModelScopeDf) return(modelDummy) } updateAllModelsResults <- function(allModelsResults,model.index,resultTable, allModelsList, rankType){ result <- as.data.frame(t(extractModelParameterValue(allModelsResults[[length(allModelsResults)]]))) modelNumber <- strsplit(as.character(resultTable$`Model No`[model.index]), split = "_") modelNumber <- as.numeric(modelNumber[[1]][2]) allModelsList[[modelNumber]] <- allModelsList[[modelNumber]]+1 result <- cbind(nrow(resultTable)+1 ,Model_No = paste0("CANDIDATE_",modelNumber,"_Dummy_",as.numeric(allModelsList[[modelNumber]])), result) colnames(result) <- c("Index","Model No","%R2","%R2.adj","2-DW","T.stat.avg","VIF.Avg","RootMSE","F_Stat","MAPE") result$`%R2` <- sapply(result$`%R2`, function(x) x <- round((x * 100),digits = 2)) result$`%R2.adj` <- sapply(result$`%R2.adj`, function(x) x <- round((x * 100),digits = 2)) if(length(resultTable)==8){ result <- rbind(resultTable, result) } else { result <- rbind(resultTable[,!names(resultTable) %in% c("Model_Rank","Model_Score")], result) } rankResultList <- rankModels(result, rankType) ranked_result <- cbind(result, Model_Rank = rankResultList$rank, Model_Score = rankResultList$score) ranked_result$Model_Score <- round(ranked_result$Model_Score,digits = 2) resultList <- as.list(NULL) resultList[["ranked_result"]] <- ranked_result resultList[["allModelsList"]] <- allModelsList return(resultList) } allPossibleRegressions <- function(modelScopeDf, baseFormula, parametersDf){ n <- nrow(modelScopeDf) modelScopeDfFinal <- modelScopeDf modelScopeDfFinal$period <- NULL modelScopeDfFinal <- as.data.frame(lapply(modelScopeDfFinal, function(x) as.numeric(as.character(x)))) allModelsResults <<- lapply(baseFormula,function(x, data) lm(x, data=modelScopeDfFinal),data=modelScopeDfFinal) allModelsResults1 <- modelFilterByTStat(allModelsResults, parametersDf) allModelsResults <- allModelsResults1 result <- NULL result[["ModelList"]] <- allModelsResults result[["result"]] <- extractModelParameter(allModelsResults) return(result) } # Function to extract model parameters to display on screen extractModelParameter <- function(allModelsResults){ #allModelsResults <- dummyModel # calculating number of models n.models <- length(allModelsResults) if(n.models == 0){ # if there is no model, then message will display to change parameter. return(0) }else{ # calling the function to extract the parameter of each model to rank. result <- lapply(allModelsResults, extractModelParameterValue) result <- as.data.frame(matrix(unlist(result), nrow=n.models, byrow=T)) result <- cbind(index = c(1:nrow(result)),paste0("CANDIDATE_",1:nrow(result)), result) rownames(result) <- NULL colnames(result) <- c("Index","Model No","%R2","%R2.adj","2-DW","T.stat.avg","VIF.Avg","RootMSE","F_Stat","MAPE") result$`%R2` <- sapply(result$`%R2`, function(x) x <- round((x * 100),digits = 2)) result$`%R2.adj` <- sapply(result$`%R2.adj`, function(x) x <- round((x * 100),digits = 2)) return(result) } } # fucntion to extract model parameter to rank the model extractModelParameterValue <- function(fit) { R2 <- summary(fit)$r.squared R2.adj <- summary(fit)$adj.r.squared dw <- abs(2-durbinWatsonTest(fit)[[2]]) model_t_stat_avg <- mean(abs(tidy(fit)$statistic)) VIF.Avg <- mean(abs(vif(fit))) RootMSE <- sqrt(mean(fit$residuals^2)) F_Stat <- round(summary(fit)$fstatistic[1],digits = 5) MAPE <- mape(y = fit$fitted.values, x = fit$model[,1]) out <- data.frame(R2=R2, R2.adj=R2.adj,DurbinWatson=dw, T.Stat.Avg = model_t_stat_avg, VIF.Avg = VIF.Avg,RootMSE = RootMSE, F_Stat = F_Stat, MAPE = MAPE) out <- sapply(out,function(x) if(!is.nan(x)) {x <- x} else{x <- 0} ) return(out) } # check the model tstat of tstat dir variable to filter out others models from allmodelresults. modelFilterByTStat <- function(allModelsResults,parametersDf){ # extarcting variable name with tStatDir from parameterDF by only taking only non zero tStatDir variables to filter the models. tstatParameterDF <- parametersDf[which(parametersDf$TstatDir != 0) ,c(1,which(colnames(parametersDf)=="TstatDir"))] # Function to check the model if tstat of model term should be greater than the tStatDir value provided by user for positive tstat direction or if tstat of model term should be lesser than the tStatDir value provided by user for negative tstat direction. tStatCheck <- function(modelIndex, tstatParam){ modelData <- allModelsResults[[modelIndex]] modelDf <- tidy(modelData)[c(1,4)] modelTerm <- gsub("_L+[0-9].*", "", modelDf$term) flag <- 0 if(all(tstatParam$VariableName %in% modelTerm)){ for (i in 1:nrow(tstatParam)) { if(tstatParam$TstatDir[i] < 0 & round(modelDf[grep(tstatParam$VariableName[i], modelTerm),2],5) < 0 & abs(round(modelDf[grep(tstatParam$VariableName[i], modelTerm),2],5)) > abs(round(as.numeric(as.character(tstatParam$TstatDir[i])),2))){ flag <- flag + 1 }else if(tstatParam$TstatDir[i] > 0 & round(modelDf[grep(tstatParam$VariableName[i], modelTerm),2],5) > round(as.numeric(as.character(tstatParam$TstatDir[i])),2)){ flag <- flag + 1 } } if(flag == nrow(tstatParam)){ return(modelIndex) } }else { # here return modelindex which doesn't have tsat derived variable. return(modelIndex) } } if(nrow(tstatParameterDF) >= 1){ tstatModel <- lapply(1:nrow(tstatParameterDF),function(y){ unlist(sapply(1:length(allModelsResults), function(x, tstatParam){tStatCheck(x,tstatParam)}, tstatParam = tstatParameterDF[y,])) }) tstatFilteredModelIndex <- Reduce(intersect, tstatModel) tstatFinalModel <- allModelsResults[tstatFilteredModelIndex] return(tstatFinalModel) }else{ return(allModelsResults) } } #Function definition to Ranking the Model based on score rankModels <- function(data, rankType) { if(nrow(data) == 1 |nrow(data) == 0){ return(list(0)) }else{ if(rankType == "Ranking1"){ #Ranking formula is used by Nimish's team factors_for_ranking <- data factors_for_ranking$RankAverage <- factors_for_ranking$`%R2` - factors_for_ranking$`%R2.adj` factors_for_ranking <- factors_for_ranking[order(factors_for_ranking$RankAverage,-factors_for_ranking$F_Stat),] factors_for_ranking <- within(factors_for_ranking, FinalRankForModels <- rank(order(factors_for_ranking$RankAverage,-factors_for_ranking$F_Stat), ties.method='average')) factors_for_ranking <- factors_for_ranking[order(factors_for_ranking$Index),] return(list(rank=factors_for_ranking$FinalRankForModels,score =factors_for_ranking$RankAverage)) }else if(rankType == "Ranking2"){ # Ranking formula is used by Sounava's team factors_for_ranking <- data # creating ranks # ranks based on R Square, Adjusted R square and Tstat Average in descending order RankedModels_based_on_Parameters <- as.data.frame( apply(cbind(factors_for_ranking$R2,factors_for_ranking$R2.adj,factors_for_ranking$T.stat.avg), 2,FUN=function(x){ rank(-x,ties.method = "average") })) #Normalizing DW statistic by negating all the values from 2. factors_for_ranking$DW_normalized <- ave(factors_for_ranking$DW,FUN=function(y){2-y}) # Ranks based on DW and RMSE in ascending order ReverseRanking_Models <- as.data.frame( apply(cbind(factors_for_ranking$RootMSE,factors_for_ranking$DW_normalized),2, FUN=function(z){ rank(z,ties.method = "average") })) # Final ranks in a dataframe FinaldataforRanking <- cbind(RankedModels_based_on_Parameters,ReverseRanking_Models) #Averaging the ranks across parameters for models factors_for_ranking$RankAverage <- apply(FinaldataforRanking,1,mean) #Final Ranks for Models based on the average of all the statistical parameters factors_for_ranking <- transform( factors_for_ranking,FinalRankForModels = rank(factors_for_ranking$RankAverage,ties.method = "average") ) # rounding off model rank to it's floor value. factors_for_ranking$FinalRankForModels <- floor(factors_for_ranking$FinalRankForModels) return(list(rank=factors_for_ranking$FinalRankForModels,score =factors_for_ranking$RankAverage)) } } } sortModelResult <- function(modelResult,flag){ if(flag == TRUE){ resultTableDF <- arrange(modelResult, Model_Rank) }else{ resultTableDF <- modelResult } return(resultTableDF) } # Function to create bucket wise variable name in data frame to filter the model based on variables. createBucketVarData <- function(amInputBuckets){ amInputBuckets <- amInputBuckets[!which(amInputBuckets$bucket=="Dependent"),] bucketList <- split(amInputBuckets, by = "bucket",keep.by = FALSE) ## Compute maximum length of each bucket bucketVarLength <- as.vector(NULL) for (i in 1:length(bucketList)) { bucketVarLength <- append(bucketVarLength, length(bucketList[[i]]$variableName)) } max.bucketLen <- max(bucketVarLength) ## Add NA values to list elements for (i in 1:length(bucketList)) { bucketList[[i]] <- lapply(bucketList[[i]], function(v) { c(v, rep(NA, max.bucketLen-length(v)))}) } bucketVar <- as.data.frame.list(bucketList) colnames(bucketVar) <- names(bucketList) return(bucketVar) } # Function to extract variable name from all generated models through regression. extractModelVarName <- function(allModelsResults){ modelVarNames <- list() # extracting model variable with model index from all model results. for (i in 1:length(allModelsResults)) { test <- allModelsResults[[i]] temp <- names(test$model) temp[grep("Dummy_Var",temp)] <- NA #modelVarNames[[i]] <- c(i,names(test$model)) modelVarNames[[i]] <- c(i,temp) } ## Compute maximum length max.length <- max(sapply(modelVarNames, length)) ## Add NA values to model variable list elements to make same length modelVarNames <- lapply(modelVarNames, function(v) { c(v, rep(NA, max.length-length(v)))}) modelVar <- do.call(rbind.data.frame, modelVarNames) colnames(modelVar) <- paste("Var", 1:ncol(modelVar), sep="") if(length(allModelsResults)== 1){ return(modelVar) }else { # removing lag, decay parts from model variable names. modelVar <- as.data.frame(apply(modelVar, 2, function(x){ x <- gsub("_L.*","",as.character(x)) })) return(modelVar) } } # Function to extract allModelResults filtered by variable given by user. extractFilterModelResults <- function(bucketSelectedVarName, modelVar, allModelsResults, resultTable){ test <- bucketSelectedVarName filterResultTable <- data.frame() if(length(test)< length(modelVar) & length(test)>=2){ testResult <- as.data.frame(t(apply(modelVar[,2:(length(test)+1)], 1, function(x){ x %in% test }))) testResult <- cbind(ModelIndex = modelVar$Var1, testResult) testResult$TrueNumber <- apply(testResult[,2:(length(test)+1)],1,function(x){ length(which(x==TRUE)) }) modelFilter <- as.numeric(as.character(testResult$ModelIndex[which(testResult$TrueNumber == length(test))])) if(length(modelFilter) == 0){ return(filterResultTable) }else{ filterResultTable <- resultTable[modelFilter, ] filterResultTable[,3:7] <- sapply(filterResultTable[,3:7], function(x) round(x, digits = 5)) return(filterResultTable) } }else{ return(filterResultTable) } } # Function to extract top model for each variable combination. extractVarCombModel <- function(modelVarCombList, modelVarCombIndex, rankType){ n.models <- length(modelVarCombList) # calling the function to extract the parameter of each model to rank. result <- lapply(modelVarCombList, extractModelParameterValue) result <- as.data.frame(matrix(unlist(result), nrow=n.models, byrow=T)) result <- cbind(paste("CANDIDATE_",modelVarCombIndex), result) rownames(result) <- NULL colnames(result) <- c("Model No","%R2","%R2.adj","2-DW","T.stat.avg","VIF.Avg","RootMSE", "F_Stat","MAPE") result$`%R2` <- sapply(result$`%R2`, function(x) x <- round((x * 100),digits = 2)) result$`%R2.adj` <- sapply(result$`%R2.adj`, function(x) x <- round((x * 100),digits = 2)) if(nrow(result)<=1){ ranked_result = result }else { rankResult <- rankModels(result, rankType) ranked_result <- cbind(result, Model_Rank = rankResult$rank, Model_Score = rankResult$score) } if(nrow(ranked_result) >= 2){ topModelVarCombResult <- arrange(ranked_result, desc(Model_Score)) return(topModelVarCombResult[1,]) }else{ return(ranked_result) } } # function to get the unique variable combination table. getModelVarTable <- function(allModelsResults){ modelVar <- extractModelVarName(allModelsResults) # to remove the duplicate combination modelVarComb <- modelVar[,-1] modelVarComb <- modelVarComb[!duplicated(modelVarComb),] if(nrow(modelVarComb) == 1){ modelVarComb <- as.data.frame(t(apply(modelVarComb, 2, as.character)),stringsAsFactors = FALSE) }else { modelVarComb <- as.data.frame(apply(modelVarComb, 2, as.character),stringsAsFactors = FALSE) } return(modelVarComb) } # function to extract Top model for exch variable combination in list. extractTopModelVariableCombResult <- function(allModelsResults, resultTableDetail, resultTable){ modelVarComb <- getModelVarTable(allModelsResults) modelVar <- extractModelVarName(allModelsResults) varCombList <- list() for (i in 1:nrow(modelVarComb)) { varCombList[[i]] <- modelVarComb[i,] varCombList[[i]] <- unlist(lapply(varCombList[[i]], na.omit)) } varCombList <<- varCombList varCombModelList <- list() for (i in 1:length(varCombList)) { test <- varCombList[[i]] testResult <- as.data.frame(t(apply(modelVar[,2:(length(test)+1)], 1, function(x){ x %in% test }))) testResult <- cbind(ModelIndex = modelVar$Var1, testResult) testResult$TrueNumber <- apply(testResult[,2:(length(test)+1)],1,function(x){ length(which(x==TRUE)) }) varCombModelList[[i]] <- as.numeric(as.character(testResult$ModelIndex[which(testResult$TrueNumber == length(test))])) } varCombModelList modelCombList <- list() for (i in 1:length(varCombModelList)) { if(length(varCombModelList[[i]])>1){ modelCombList[i] <- sortModelResult(resultTableDetail[varCombModelList[[i]],],flag = 1)[1,1] }else { modelCombList[i] <- resultTable[varCombModelList[[i]][1],1] } } return(modelCombList) } # function to compare model. compareModelResult <- function(s, allModelsResults, resultTableDetail, parametersDf){ models <- allModelsResults[s] compareModel <- NULL for (i in 1:length(models)) { temp <- data.frame(getElasticity(models[[i]],parametersDf = parametersDf),row.names = NULL) temp <- temp[,-c(3,5,6)] temp$Model <- resultTableDetail[s[i],c("Model No")] temp<- temp[,c(7,1:6)] if(i==1){ compareModel <- temp }else { compareModel <- rbind(compareModel, temp) } } return(compareModel) } extractModelDetail <- function(model, modelScopeDf, parametersDf, modelResult, RegDataTemp, dummyModelScopeDf){ tmpModelScopeDf <- modelScopeDf if(any(grepl("Dummy",names(model$coefficients)))){ dummyModelIndex <- which(as.character(dummyModelScopeDf$Model_No) == modelResult[,2]) tmpModelScopeDf <- subset(tmpModelScopeDf, period >= dummyModelScopeDf[dummyModelIndex,"Start_Date"] & period <= dummyModelScopeDf[dummyModelIndex,"End_Date"]) } modelParameters <- getElasticity(model,parametersDf) output <- NULL output <- c(output,"The REG Procedure") output <- c(output,"\n\n") output <- c(output,paste("Model:",modelResult$`Model No`)) output <- c(output,paste("Dependant Variable:",names(model$model[1]))) output <- c(output,"\n\n") output <- c(output,paste("Number of Observations Read:",nrow(RegDataTemp[-1,]))) output <- c(output,paste("Number of Observations Used:",nrow(tmpModelScopeDf))) output <- c(output,"\n\n") output <- c(output,noquote(capture.output(write.csv(modelResult,stdout(),row.names = F,quote = F)))) output <- c(output,"\n\n") output <- c(output,noquote(capture.output(write.csv(modelParameters,file = stdout(),row.names = F,quote = F)))) output <- c(output,"\n\n") output <- as.data.frame(output,quote=F) colnames(output) <- "output" resultOutput <- list() resultOutput[["modelDetails"]] <- cSplit(output,"output",sep = ",",type.convert = F) resultOutput[["actPredData"]] <- getActualVsPredictedDf(tmpModelScopeDf,model) return(resultOutput) } extractModelData <- function(model,modelScopeDummyTable, modelScopeDf, parametersDf,dummyModelScopeDf,modelResult){ if(any(grepl("Dummy",names(model$coefficients)))){ dummyModelIndex <- which(as.character(dummyModelScopeDf$Model_No) == modelResult[,2]) Period <- modelScopeDf[which(modelScopeDf$period >= dummyModelScopeDf[dummyModelIndex,"Start_Date"] & modelScopeDf$period <= dummyModelScopeDf[dummyModelIndex,"End_Date"]),"period"] }else{ Period <- modelScopeDf$period } modelData <- cbind(Period,model$model) return(modelData) } ################################################################################ ##################### OLS manual process Acquire ########################### ################################################################################ ##################### Function related to Model Manager ######################## olsm_createModelManagerData <- function(olsm_SelectedVar){ df <- data.frame( VariableName = olsm_SelectedVar, #Variable type Type = factor( rep("Not in Model", times = length(olsm_SelectedVar)), levels = c("DepVar", "Manual No Trans", "Outside No Trans","Fixed Var No Trans","Manual TOF", "Outside TOF","Fixed Var TOF","Not in Model") ), #Variable transformation Transformation = factor( rep("Linear", times = length(olsm_SelectedVar)), levels = c("Linear", "S-Curve","S-origin","Power","Testing") ), #Decay type Decay = factor(rep("Media", times = length(olsm_SelectedVar)),levels = c("Media", "Promo")), #Lag minimum LagMin = rep(as.integer(0), times = length(olsm_SelectedVar)), #Lag maximum LagMax = rep(as.integer(0), times = length(olsm_SelectedVar)), #decay steps DecaySteps = rep(as.integer(1), times = length(olsm_SelectedVar)), #decay minimum DecayMin = rep(as.numeric(1), times = length(olsm_SelectedVar)), #decay maximum DecayMax = rep(as.numeric(1), times = length(olsm_SelectedVar)), #alpha steps AlphaSteps = rep(as.integer(1), times = length(olsm_SelectedVar)), #alpha minimum AlphaMin = rep(as.numeric(0), times = length(olsm_SelectedVar)), #alpha maximum AlphaMax = rep(as.numeric(0), times = length(olsm_SelectedVar)), #alpha minimum BetaMin = rep(as.numeric(1), times = length(olsm_SelectedVar)), #Series Multipler BetaMultiplier = rep(as.integer(0), times = length(olsm_SelectedVar)), #alpha steps BetaSteps = rep(as.integer(1), times = length(olsm_SelectedVar)), #Series maximum SeriesMax = rep(as.numeric(1), times = length(olsm_SelectedVar)), # Normalization Normalization = factor( rep("None", times = length(olsm_SelectedVar)), levels = c("None", "Division","Subtraction") ), # Min Max Adjustment Min_Max_Adjustment = factor( rep("None", times = length(olsm_SelectedVar)), levels = c("None","Min","Max","Average") ), # Fixed Coefficient Fixed_Coefficient = rep(as.numeric(0), times = length(olsm_SelectedVar)), # Combined Column Combined_Column = rep(as.integer(0), times = length(olsm_SelectedVar)), # Mixed Effect Random_Effect = factor( rep(0, times = length(olsm_SelectedVar)), levels = c(0,1) ), stringsAsFactors = F ) return(df) } ##################### Function related to Transformation ####################### createOlsmTransformation <- function(olsm_RegDataModelList, olsm_parametersDF,modelFeatureList){ olsm_RegDataModelDF <- olsm_RegDataModelList[[modelFeatureList$TransGeo]] ## condition to check Transformation, Media Decay or Promo Decay and call respective functions callDecayfunctions <- function(i,olsm_parametersDF, df_laggedDT, decayMin){ # i<- 1 if(olsm_parametersDF[i,"Decay"] == "Media"){ #capturing Media Decay data df_laggedDT <- olsmCalcMediaDecay(col = df_laggedDT,decay = decayMin) }else if(olsm_parametersDF[i,"Decay"] == "Promo"){ #capturing Promo Decay data df_laggedDT <- olsmCalcPromoDecay(df_laggedDT,decayMin) } return(df_laggedDT) } #Not in the model dropping varsToBeDropped <- NULL varsToBeDropped <- olsm_parametersDF$VariableName[olsm_parametersDF$Type == "Not in Model"] if(length(varsToBeDropped)!=0){ tempData <- olsm_RegDataModelDF[,-which(colnames(olsm_RegDataModelDF) %in% varsToBeDropped)] }else { tempData <- olsm_RegDataModelDF } TransformedDf <- tempData[,which(names(tempData) %in% c("Geography","Period"))] for(i in 1:nrow(olsm_parametersDF)){ # i =4 name <- as.character(olsm_parametersDF[i,"VariableName"]) olsm_varDetails <- as.list(olsm_parametersDF[i,]) olsm_varDetails[["elasticityFlag"]] <- modelFeatureList$elasticityFlag olsm_varDetails[["elasticityValue"]] <- modelFeatureList$elasticityValue olsm_varDetails[["elasticityL12Flag"]] <- modelFeatureList$elasticityL12Flag # this will execute only for scurve and sorigin transformation for SeriesMax and ScurveVarMax if(any(as.character(olsm_parametersDF[i,"Transformation"]) %in% c("S-Curve","Testing","S-origin")) & olsm_parametersDF[i,"Type"] != "Not in Model" ){ # Changes included to implement SeriesMax by geography or All. if(modelFeatureList$ScurveSeriesMaxChoice == "All"){ olsm_varDetails[["SeriesMax"]] <- as.numeric(as.character(olsm_parametersDF$SeriesMax[i])) }else if(modelFeatureList$ScurveSeriesMaxChoice == "Geo"){ seriesMaxDF <- modelFeatureList$ScurveSeriesMaxDF olsm_varDetails[["SeriesMax"]] <- seriesMaxDF[seriesMaxDF$Geography == modelFeatureList$TransGeo & seriesMaxDF$VariableName == name,"SeriesMax"] } if(olsm_varDetails[["SeriesMax"]] == 0){ olsm_varDetails[["SeriesMax"]] = 0.01 } # Taking raw data to calculate max which is used in s-curve formula. # Changes included to implement variable Max from raw data by geography or All. if(modelFeatureList$ScurveVarMaxChoice == "All"){ olsm_varDetails[["ScruveVarMax"]] <- max(rbindlist(olsm_RegDataModelList)[,..name],na.rm = T) }else if(modelFeatureList$ScurveVarMaxChoice == "Geo"){ olsm_varDetails[["ScruveVarMax"]] <- max(tempData[, name],na.rm = T) } } # S-Shaped_New --- Currently ME will not do any transformation for S-Shaped_New. Not implemented Yet. if(olsm_parametersDF[i,"Type"] %in% c("DepVar","Fixed Var No Trans","Manual No Trans","Outside No Trans")){ transVec <- as.data.frame(tempData[,colnames(tempData) %in% olsm_parametersDF[i,"VariableName"]]) if(olsm_varDetails$elasticityFlag == TRUE){ transVec <- transVec + (transVec*as.numeric(olsm_varDetails$elasticityValue/100)) } colnames(transVec) <- name TransformedDf<-cbind(TransformedDf,transVec) } else if(olsm_parametersDF[i,"Type"] %in% c("Manual TOF","Fixed Var TOF")){ transVec <- as.data.frame(tempData[, name]) colnames(transVec) <- name #apply lag over the transVec data dfDT <- data.table(name = transVec) names(dfDT) <- name dfDTElastic <- dfDT # increase the full data by elasticity value if elasticityFlag is true. if(olsm_varDetails$elasticityFlag == TRUE){ dfDTElastic <- dfDTElastic + (dfDTElastic*as.numeric(olsm_varDetails$elasticityValue/100)) } ModellingPeriodData <- NULL # increase the last 12 months data by elasticity value if elasticityL12Flag is true. if(olsm_varDetails$elasticityL12Flag == TRUE){ # Modelling Period ModellingPeriodData <- zoo::as.yearmon(dmy(modelFeatureList$modellingPeriod)) # Af period tempL12 <- data.frame(Period = zoo::as.yearmon(dmy(tempData[,"Period"])),dfDT) ModellingPeriodData <- data.frame(Period = tempL12$Period[which(tempL12$Period %in% ModellingPeriodData)],stringsAsFactors = F) tempL12[which(tempL12$Period %in% tail(unique(ModellingPeriodData$Period),n = 12)),name] <- tempL12[which(tempL12$Period %in% tail(unique(ModellingPeriodData$Period),n = 12)),name] + (tempL12[which(tempL12$Period %in% tail(unique(ModellingPeriodData$Period),n = 12)),name] * as.numeric(olsm_varDetails$elasticityValue/100)) dfDTElastic <- data.table(tempL12[,name]) colnames(dfDTElastic) <- name } dfDT <- dfDTElastic df_laggedDT <- dfDT[,(name):=shift(dfDT[[name]],olsm_varDetails$LagMin,fill = 0,type = "lag")] if(olsm_parametersDF[i,"Transformation"] == "S-Curve" | olsm_parametersDF[i,"Transformation"] == "Testing"){ if(modelFeatureList$adStockChoice == "AdStock First"){ df_laggedDT <- callDecayfunctions(i,olsm_parametersDF, df_laggedDT, olsm_varDetails$DecayMin) if(olsm_varDetails$ScruveVarMax != 0){ set(x = df_laggedDT,j = name,value = (as.numeric(olsm_varDetails$BetaMin)/(10^10))^(as.numeric(olsm_varDetails$AlphaMin)^((as.numeric(df_laggedDT[[name]])/(olsm_varDetails$ScruveVarMax * olsm_varDetails$SeriesMax))*100))) }else { df_laggedDT[[name]] <- 0 } }else if(modelFeatureList$adStockChoice == "AdStock Last"){ if(olsm_varDetails$ScruveVarMax != 0){ set(x = df_laggedDT,j = name,value = (as.numeric(olsm_varDetails$BetaMin)/(10^10))^(as.numeric(olsm_varDetails$AlphaMin)^((as.numeric(df_laggedDT[[name]])/(olsm_varDetails$ScruveVarMax * olsm_varDetails$SeriesMax))*100))) }else { df_laggedDT[[name]] <- 0 } df_laggedDT <- callDecayfunctions(i,olsm_parametersDF, df_laggedDT, decayMin = olsm_varDetails$DecayMin) } # Updating Testing transformation value with cap of 1 incase it is greater than 1. if(olsm_parametersDF$Transformation[i] == "Testing"){ df_laggedDT[df_laggedDT > 1] <- 1 } TransformedDf<-cbind(TransformedDf,as.data.frame(df_laggedDT)) }else if(olsm_parametersDF[i,"Transformation"] == "S-origin"){ if(modelFeatureList$adStockChoice == "AdStock First"){ df_laggedDT <- callDecayfunctions(i,olsm_parametersDF, df_laggedDT, olsm_varDetails$DecayMin) if(olsm_varDetails$ScruveVarMax != 0){ set(x = df_laggedDT,j = name,value = ((as.numeric(olsm_varDetails$BetaMin)/(10^9))^(as.numeric(olsm_varDetails$AlphaMin)^((as.numeric(df_laggedDT[[name]])/( olsm_varDetails$ScruveVarMax * olsm_varDetails$SeriesMax))*100)) - (as.numeric(olsm_varDetails$BetaMin)/(10^9)))) }else { df_laggedDT[[name]] <- 0 } }else if(modelFeatureList$adStockChoice == "AdStock Last"){ if(olsm_varDetails$ScruveVarMax != 0){ set(x = df_laggedDT,j = name,value = ((as.numeric(olsm_varDetails$BetaMin)/(10^9))^(as.numeric(olsm_varDetails$AlphaMin)^((as.numeric(df_laggedDT[[name]])/( olsm_varDetails$ScruveVarMax * olsm_varDetails$SeriesMax))*100)) - (as.numeric(olsm_varDetails$BetaMin)/(10^9)))) }else { df_laggedDT[[name]] <- 0 } df_laggedDT <- callDecayfunctions(i,olsm_parametersDF, df_laggedDT, olsm_varDetails$DecayMin) } TransformedDf<-cbind(TransformedDf,as.data.frame(df_laggedDT)) }else if (olsm_parametersDF[i,"Transformation"] == "Power"){ if(modelFeatureList$adStockChoice == "AdStock First"){ df_laggedDT <- callDecayfunctions(i,olsm_parametersDF, df_laggedDT,as.numeric(olsm_varDetails$DecayMin)) set(df_laggedDT,j = name,value=df_laggedDT[[name]]^as.numeric(olsm_varDetails$AlphaMin)) }else if(modelFeatureList$adStockChoice == "AdStock Last"){ set(df_laggedDT,j = name,value=df_laggedDT[[name]]^as.numeric(olsm_varDetails$AlphaMin)) df_laggedDT <- callDecayfunctions(i,olsm_parametersDF, df_laggedDT, as.numeric(olsm_varDetails$DecayMin)) } TransformedDf<-cbind(TransformedDf,as.data.frame(df_laggedDT)) }else if(olsm_parametersDF[i,"Transformation"] %in% c("Linear")){ df_laggedDT <- callDecayfunctions(i,olsm_parametersDF, df_laggedDT, olsm_varDetails$DecayMin) TransformedDf<-cbind(TransformedDf,df_laggedDT) } } else if(olsm_parametersDF[i,"Type"] == "Outside TOF"){ TransformedDf<-cbind(TransformedDf,createOlsmTransOutsideTOF(olsm_RegDataTemp = tempData, olsm_parametersDF,name,olsm_varDetails,modelFeatureList)) } } return(TransformedDf) } createOlsmTransOutsideTOF <- function(olsm_RegDataTemp, olsm_parametersDF, name, olsm_varDetails,modelFeatureList){ #tempolsm_varDetails <- olsm_varDetails TransformedOutsideTOF_Df <- NULL transVec <- olsm_RegDataTemp[,name] transElastic <- transVec # increase the full data elasticity value if elasticity flag is true. if(olsm_varDetails$elasticityFlag == TRUE){ transElastic <- transVec + (transVec*as.numeric(olsm_varDetails$elasticityValue/100)) } ModellingPeriodData <- NULL # increase the last 12 months data by elasticity value if elasticityL12Flag is true. if(olsm_varDetails$elasticityL12Flag == TRUE){ ModellingPeriodData <- zoo::as.yearmon(dmy(modelFeatureList$modellingPeriod)) tempL12 <- data.frame(Period = zoo::as.yearmon(dmy(olsm_RegDataTemp[,"Period"])),transElastic) ModellingPeriodData <- data.frame(Period = tempL12$Period[which(tempL12$Period %in% ModellingPeriodData)],stringsAsFactors = F) tempL12[which(tempL12$Period %in% tail(unique(ModellingPeriodData$Period),n = 12)),-1] <- tempL12[which(tempL12$Period %in% tail(unique(ModellingPeriodData$Period),n = 12)),-1] + (tempL12[which(tempL12$Period %in% tail(unique(ModellingPeriodData$Period),n = 12)),-1] * as.numeric(olsm_varDetails$elasticityValue/100)) transElastic <- data.frame(tempL12[,-1]) colnames(transElastic) <- names(transVec) } transVec <- as.data.frame(transElastic) names(transVec) <- name lagTrans <- olsmCreateLagSeries(name = name, df = transVec[,name], olsm_varDetails$LagMin, olsm_varDetails$LagMax) if(as.character(olsm_varDetails$Transformation) == "S-Curve" | as.character(olsm_varDetails$Transformation) == "Testing"){ if(modelFeatureList$adStockChoice == "AdStock First"){ TransformedOutsideTOF_Df <- olsmdecayAlphaTrans(olsm_RegDataTemp,lagTrans,name, olsm_varDetails) }else if (modelFeatureList$adStockChoice == "AdStock Last"){ TransformedOutsideTOF_Df <- olsmalphaDecayTrans(olsm_RegDataTemp,lagTrans,name, olsm_varDetails) } # Updating Testing transformation value with cap of 1 incase it is greater than 1. if(as.character(olsm_varDetails$Transformation) == "Testing"){ TransformedOutsideTOF_Df[TransformedOutsideTOF_Df > 1] <- 1 } }else if(as.character(olsm_varDetails$Transformation) == "S-origin"){ if(modelFeatureList$adStockChoice == "AdStock First"){ TransformedOutsideTOF_Df <- olsmdecayAlphaSoriginTrans(olsm_RegDataTemp,lagTrans,name, olsm_varDetails) }else if (modelFeatureList$adStockChoice == "AdStock Last"){ TransformedOutsideTOF_Df <- olsmalphaDecaySoriginTrans(olsm_RegDataTemp,lagTrans,name, olsm_varDetails) } }else if(as.character(olsm_varDetails$Transformation) == "Power"){ if(modelFeatureList$adStockChoice == "AdStock First"){ TransformedOutsideTOF_Df <- olsmdecayPowerTrans(olsm_RegDataTemp,lagTrans,name, olsm_varDetails) }else if (modelFeatureList$adStockChoice == "AdStock Last"){ TransformedOutsideTOF_Df <- olsmpowerDecayTrans(olsm_RegDataTemp,lagTrans,name, olsm_varDetails) } }else if(as.character(olsm_varDetails$Transformation) == "Linear"){ TransformedOutsideTOF_Df<-olsmDecayTrans(olsm_RegDataTemp,lagTrans,name, olsm_varDetails) } return(as.data.frame(TransformedOutsideTOF_Df)) } # Media Decay for Manual TOF olsmCalcMediaDecay <- function(col,decay){ name <- colnames(col) col = unlist(col,use.names = FALSE) for(i in 1:length(col)){ if(i ==1){ col[i] <- as.numeric(col[i]) } else if(!is.na(col[i - 1])){ col[i] <- as.numeric(col[i])+ as.numeric(col[i - 1]*(1-decay)) } } col <- data.table(col,stringsAsFactors = FALSE) colnames(col)<- name return(col) } # Promo Decay for Manual TOF olsmCalcPromoDecay <- function(col,decay){ name <- colnames(col) col = unlist(col,use.names = FALSE) for(i in 1:length(col)){ if(i ==1){ col[i] <- as.numeric(col[i]) } else if(!is.na(col[i - 1])){ col[i] <- ((decay*as.numeric(col[i]))+ (as.numeric(col[i - 1]*(1-decay)))) } } col <- data.table(col,stringsAsFactors = FALSE) colnames(col)<- name return(col) } olsmCreateLagSeries <- function(name, df, lagMin, lagMax){ lagTrans <- as.data.frame(replicate(as.numeric(as.character(df)), n = (as.numeric(as.character(lagMax))-as.numeric(as.character(lagMin)))+1),stringsAsFactors = F) lagSeries <- as.numeric(as.numeric(as.character(lagMin)):as.numeric(as.character(lagMax))) names(lagSeries) <- paste0(name,"_L",as.numeric(as.character(lagMin)):as.numeric(as.character(lagMax))) names(lagTrans) <- names(lagSeries) df_Lag <- as.data.table(lagTrans) for(name in names(lagSeries)){ if(!is.na(lagSeries[name])) { df_Lag[,(name):=shift(df_Lag[[name]],as.numeric(unname(lagSeries[name])),fill = 0,type = "lag")] } } df_lagged <- as.data.frame(df_Lag) return(df_lagged) } olsmgetAlpha <- function(df_lagged,varMax,alpha,beta,df_variable, seriesMax){ #Refresnce formula: (beta/(10^10))^(as.numeric(unname(alpha[name]))^((df_lagged[,name]/max(df_lagged[,name]))*100)) df_laggedDT <- as.data.table(df_lagged) beta <- beta[complete.cases(beta)] for(name in names(beta)){ if(varMax != 0){ set(x = df_laggedDT,j = name,value = (as.numeric(unname(beta[name]))/(10^10))^((as.numeric(unname(alpha))^((as.numeric(df_laggedDT[[name]])/(varMax * seriesMax))*100)))) } else{ df_laggedDT[[name]] <- 0 } } df <- as.data.frame(df_laggedDT) return(df) } # alpha for S-origin type olsmgetS_OriginAlpha <- function(df_lagged,varMax,alpha,beta,df_variable, seriesMax){ #Refresnce formula: (beta/(10^10))^(as.numeric(unname(alpha[name]))^((df_lagged[,name]/max(df_lagged[,name]))*100))- - (beta/(10^9)) df_laggedDT <- as.data.table(df_lagged) beta <- beta[complete.cases(beta)] for(name in names(beta)){ if(varMax != 0){ set(x = df_laggedDT,j = name,value = ((as.numeric(unname(beta[name]))/(10^9))^(as.numeric(unname(alpha))^((as.numeric(df_laggedDT[[name]])/(varMax*seriesMax))*100)) - (as.numeric(unname(beta[name]))/(10^9)))) } else{ df_laggedDT[[name]] <- 0 } } df <- as.data.frame(df_laggedDT) return(df) } #power olsmgetPower <- function(df,powerRange){ dfPowerDt <- as.data.table(df) powerSeries <- powerRange[complete.cases(powerRange)] for(name in names(powerSeries)) { set(dfPowerDt,j = name,value=dfPowerDt[[name]]^as.numeric(unname(powerSeries[name]))) } df <- as.list.data.frame(dfPowerDt) return(df) } #Media Decay olsmgetMediaDecay <- function(df,decay){ df_DecayDT <- as.data.table(df) decay <- decay[complete.cases(decay)] calcDecay <- function(col,decay){ for(i in 1:length(col)){ if(i ==1){ col[i] <- as.numeric(col[i]) } else if(!is.na(col[i - 1])){ col[i] <- as.numeric(col[i])+ as.numeric(col[i - 1]*(1-decay)) } } return(col) } for(name in names(decay)) { set(df_DecayDT,j = name,value=calcDecay(df_DecayDT[[name]],as.numeric(unname(decay[name])))) } df <- as.data.frame(df_DecayDT) return(df) } #promo Decay olsmgetPromoDecay <- function(df,decay){ df_DecayDT <- as.data.table(df) decay <- decay[complete.cases(decay)] calcDecay <- function(col,decay){ for(i in 1:length(col)){ if(i ==1){ col[i] <- as.numeric(col[i]) } else if(!is.na(col[i - 1])){ col[i] <- ((decay*as.numeric(col[i]))+(as.numeric(col[i - 1]*(1-decay)))) } } return(col) } for(name in names(decay)) { set(df_DecayDT,j = name,value=calcDecay(df_DecayDT[[name]],as.numeric(unname(decay[name])))) } df <- as.data.frame(df_DecayDT) return(df) } #capturing Scurve Decay data olsmalphaDecayTrans <- function(olsm_RegDataTemp,lagTrans,name, olsm_varDetails){ alphaTransformedList <- list() # Alpha for(lagName in names(lagTrans)){ #lagName <- names(lagTrans)[2] if(as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 0 | as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 1){ alphaSteps <- 1 AlphaSeries <- as.numeric(as.character(olsm_varDetails$AlphaMin)) }else { alphaSteps <- (as.numeric(as.character(olsm_varDetails$AlphaMax))-as.numeric(as.character(olsm_varDetails$AlphaMin)))/(as.numeric(as.character(olsm_varDetails$AlphaSteps))-1) AlphaSeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) } lagTransAlpha <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])),n = length(AlphaSeries)),stringsAsFactors = F) names(AlphaSeries) <- paste0(lagName,"_A",seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) names(lagTransAlpha) <- names(AlphaSeries) # Beta for (alphaName in names(lagTransAlpha)) { #alphaName <- names(lagTransAlpha)[1] if(olsm_varDetails$BetaSteps==0){ BetaSeries <- olsm_varDetails$BetaMin }else { if(olsm_varDetails$BetaMultiplier==0){ BetaSeries <- olsm_varDetails$BetaMin }else{ BetaSeries <- rep(olsm_varDetails$BetaMin*(olsm_varDetails$BetaMultiplier^(0:(olsm_varDetails$BetaSteps-1)))) } } lagTransAlphaBeta <- as.data.frame(replicate(as.numeric(as.character(lagTransAlpha[,alphaName])),n = length(BetaSeries)),stringsAsFactors = F) names(BetaSeries) <- paste0(alphaName,"_B",BetaSeries) names(lagTransAlphaBeta) <- names(BetaSeries) lagTransAlphaBeta <- olsmgetAlpha(lagTransAlphaBeta,olsm_varDetails$ScruveVarMax,AlphaSeries[[alphaName]],BetaSeries,name, olsm_varDetails$SeriesMax) #Decay for(betaName in names(lagTransAlphaBeta)){ if(as.numeric(as.character(olsm_varDetails$DecaySteps)) == 0 | as.numeric(as.character(olsm_varDetails$DecaySteps)) == 1){ decaySteps <- 1 decaySeries <- as.numeric(as.character(olsm_varDetails$DecayMin)) }else { decaySteps <- (as.numeric(as.character(olsm_varDetails$DecayMax))-as.numeric(as.character(olsm_varDetails$DecayMin)))/(as.numeric(as.character(olsm_varDetails$DecaySteps))-1) decaySeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) } lagTransAlphaBetaDecay <- as.data.frame(replicate(as.numeric(as.character(lagTransAlphaBeta[,betaName])),n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(betaName,"_D",seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) names(lagTransAlphaBetaDecay) <- names(decaySeries) ## condition to check if transformation is Media Decay or Promo Decay . if(olsm_varDetails$Decay == "Media"){ lagTransAlphaBetaDecay <- olsmgetMediaDecay(lagTransAlphaBetaDecay,decaySeries) }else if(olsm_varDetails$Decay == "Promo"){ lagTransAlphaBetaDecay <- olsmgetPromoDecay(lagTransAlphaBetaDecay,decaySeries) } alphaTransformedList <- c(alphaTransformedList,lagTransAlphaBetaDecay) } } } return(as.data.frame.list(alphaTransformedList)) } #capturing Decay Scurve data olsmdecayAlphaTrans <- function(olsm_RegDataTemp,lagTrans,name, olsm_varDetails){ alphaTransformedList <- list() # Decay for(lagName in names(lagTrans)){ if(as.numeric(as.character(olsm_varDetails$DecaySteps)) == 0 | as.numeric(as.character(olsm_varDetails$DecaySteps)) == 1){ decaySteps <- 1 decaySeries <- as.numeric(as.character(olsm_varDetails$DecayMin)) }else { decaySteps <- (as.numeric(as.character(olsm_varDetails$DecayMax))-as.numeric(as.character(olsm_varDetails$DecayMin)))/(as.numeric(as.character(olsm_varDetails$DecaySteps))-1) decaySeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) } lagTransDecay <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])), n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(lagName,"_D",seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) names(lagTransDecay) <- names(decaySeries) ## condition to check if Transformation is Media Decay and Promo Decay. if(olsm_varDetails$Decay == "Media"){ lagTransDecay <- olsmgetMediaDecay(lagTransDecay,decaySeries) }else if(olsm_varDetails$Decay == "Promo"){ lagTransDecay <- olsmgetPromoDecay(lagTransDecay,decaySeries) } #Alpha for(alphaName in names(lagTransDecay)){ if(as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 0 | as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 1){ alphaSteps <- 1 AlphaSeries <- as.numeric(as.character(olsm_varDetails$AlphaMin)) }else { alphaSteps <- (as.numeric(as.character(olsm_varDetails$AlphaMax))-as.numeric(as.character(olsm_varDetails$AlphaMin)))/(as.numeric(as.character(olsm_varDetails$AlphaSteps))-1) AlphaSeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) } lagTransDecayAlpha <- as.data.frame(replicate(as.numeric(as.character(lagTransDecay[,alphaName])),n = length(AlphaSeries)),stringsAsFactors = F) names(AlphaSeries) <- paste0(alphaName,"_A",seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) names(lagTransDecayAlpha) <- names(AlphaSeries) # Beta for (betaName in names(lagTransDecayAlpha)) { if(olsm_varDetails$BetaSteps==0){ BetaSeries <- olsm_varDetails$BetaMin }else { if(olsm_varDetails$BetaMultiplier==0){ BetaSeries <- olsm_varDetails$BetaMin }else{ BetaSeries <- rep(olsm_varDetails$BetaMin*(olsm_varDetails$BetaMultiplier^(0:(olsm_varDetails$BetaSteps-1)))) } } lagTransDecayAlphaBeta <- as.data.frame(replicate(as.numeric(as.character(lagTransDecayAlpha[,betaName])),n = length(BetaSeries)),stringsAsFactors = F) names(BetaSeries) <- paste0(betaName,"_B",BetaSeries) names(lagTransDecayAlphaBeta) <- names(BetaSeries) lagTransDecayAlphaBeta <- olsmgetAlpha(lagTransDecayAlphaBeta,olsm_varDetails$ScruveVarMax,AlphaSeries[[betaName]],BetaSeries,name, olsm_varDetails$SeriesMax) alphaTransformedList <- c(alphaTransformedList,lagTransDecayAlphaBeta) } } } return(as.data.frame.list(alphaTransformedList)) } # capturing S-origin Decay data olsmalphaDecaySoriginTrans <- function(olsm_RegDataTemp,lagTrans,name, olsm_varDetails){ alphaTransformedList <- list() # Alpha for(lagName in names(lagTrans)){ #lagName <- names(lagTrans)[3] if(as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 0 | as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 1){ alphaSteps <- 1 AlphaSeries <- as.numeric(as.character(olsm_varDetails$AlphaMin)) }else { alphaSteps <- (as.numeric(as.character(olsm_varDetails$AlphaMax))-as.numeric(as.character(olsm_varDetails$AlphaMin)))/(as.numeric(as.character(olsm_varDetails$AlphaSteps))-1) AlphaSeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) } lagTransAlpha <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])),n = length(AlphaSeries)),stringsAsFactors = F) names(AlphaSeries) <- paste0(lagName,"_A",seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) names(lagTransAlpha) <- names(AlphaSeries) # Beta for (alphaName in names(lagTransAlpha)) { #alphaName <- names(lagTransAlpha)[1] if(olsm_varDetails$BetaSteps==0){ BetaSeries <- olsm_varDetails$BetaMin }else { if(olsm_varDetails$BetaMultiplier==0){ BetaSeries <- olsm_varDetails$BetaMin }else{ BetaSeries <- rep(olsm_varDetails$BetaMin*(olsm_varDetails$BetaMultiplier^(0:(olsm_varDetails$BetaSteps-1)))) } } lagTransAlphaBeta <- as.data.frame(replicate(as.numeric(as.character(lagTransAlpha[,alphaName])),n = length(BetaSeries)),stringsAsFactors = F) names(BetaSeries) <- paste0(alphaName,"_B",BetaSeries) names(lagTransAlphaBeta) <- names(BetaSeries) lagTransAlphaBeta <- olsmgetS_OriginAlpha(lagTransAlphaBeta,olsm_varDetails$ScruveVarMax,AlphaSeries[[alphaName]],BetaSeries,name, olsm_varDetails$SeriesMax) #Decay for(betaName in names(lagTransAlphaBeta)){ if(as.numeric(as.character(olsm_varDetails$DecaySteps)) == 0 | as.numeric(as.character(olsm_varDetails$DecaySteps)) == 1){ decaySteps <- 1 decaySeries <- as.numeric(as.character(olsm_varDetails$DecayMin)) }else { decaySteps <- (as.numeric(as.character(olsm_varDetails$DecayMax))-as.numeric(as.character(olsm_varDetails$DecayMin)))/(as.numeric(as.character(olsm_varDetails$DecaySteps))-1) decaySeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) } lagTransAlphaBetaDecay <- as.data.frame(replicate(as.numeric(as.character(lagTransAlphaBeta[,betaName])),n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(betaName,"_D",seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) names(lagTransAlphaBetaDecay) <- names(decaySeries) ## condition to check if Transformation is Media Decay or Promo Decay . if(olsm_varDetails$Decay == "Media"){ lagTransAlphaBetaDecay <- olsmgetMediaDecay(lagTransAlphaBetaDecay,decaySeries) }else if(olsm_varDetails$Decay == "Promo"){ lagTransAlphaBetaDecay <- olsmgetPromoDecay(lagTransAlphaBetaDecay,decaySeries) } alphaTransformedList <- c(alphaTransformedList,lagTransAlphaBetaDecay) } } } return(as.data.frame.list(alphaTransformedList)) } # capturing Decay Alpha data for S-origin olsmdecayAlphaSoriginTrans <- function(olsm_RegDataTemp,lagTrans,name, olsm_varDetails){ alphaTransformedList <- list() # Decay for(lagName in names(lagTrans)){ if(as.numeric(as.character(olsm_varDetails$DecaySteps)) == 0 | as.numeric(as.character(olsm_varDetails$DecaySteps)) == 1){ decaySteps <- 1 decaySeries <- as.numeric(as.character(olsm_varDetails$DecayMin)) }else { decaySteps <- (as.numeric(as.character(olsm_varDetails$DecayMax))-as.numeric(as.character(olsm_varDetails$DecayMin)))/(as.numeric(as.character(olsm_varDetails$DecaySteps))-1) decaySeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) } lagTransDecay <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])), n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(lagName,"_D",seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) names(lagTransDecay) <- names(decaySeries) ## Condition to Check if Transformation is Media Decay or Promo Decay . if(olsm_varDetails$Decay == "Media"){ lagTransDecay <- olsmgetMediaDecay(lagTransDecay,decaySeries) }else if(olsm_varDetails$Decay == "Promo"){ lagTransDecay <- olsmgetPromoDecay(lagTransDecay,decaySeries) } #Alpha for(alphaName in names(lagTransDecay)){ if(as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 0 | as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 1){ alphaSteps <- 1 AlphaSeries <- as.numeric(as.character(olsm_varDetails$AlphaMin)) }else { alphaSteps <- (as.numeric(as.character(olsm_varDetails$AlphaMax))-as.numeric(as.character(olsm_varDetails$AlphaMin)))/(as.numeric(as.character(olsm_varDetails$AlphaSteps))-1) AlphaSeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) } lagTransDecayAlpha <- as.data.frame(replicate(as.numeric(as.character(lagTransDecay[,alphaName])),n = length(AlphaSeries)),stringsAsFactors = F) names(AlphaSeries) <- paste0(alphaName,"_A",seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) names(lagTransDecayAlpha) <- names(AlphaSeries) # Beta for (betaName in names(lagTransDecayAlpha)) { if(olsm_varDetails$BetaSteps==0){ BetaSeries <- olsm_varDetails$BetaMin }else { if(olsm_varDetails$BetaMultiplier==0){ BetaSeries <- olsm_varDetails$BetaMin }else{ BetaSeries <- rep(olsm_varDetails$BetaMin*(olsm_varDetails$BetaMultiplier^(0:(olsm_varDetails$BetaSteps-1)))) } } lagTransDecayAlphaBeta <- as.data.frame(replicate(as.numeric(as.character(lagTransDecayAlpha[,betaName])),n = length(BetaSeries)),stringsAsFactors = F) names(BetaSeries) <- paste0(betaName,"_B",BetaSeries) names(lagTransDecayAlphaBeta) <- names(BetaSeries) lagTransDecayAlphaBeta <- olsmgetS_OriginAlpha(lagTransDecayAlphaBeta,olsm_varDetails$ScruveVarMax,AlphaSeries[[betaName]],BetaSeries,name,olsm_varDetails$SeriesMax) alphaTransformedList <- c(alphaTransformedList,lagTransDecayAlphaBeta) } } } return(as.data.frame.list(alphaTransformedList)) } #capturing Power Decay data olsmpowerDecayTrans <- function(olsm_RegDataTemp,lagTrans,name, olsm_varDetails){ powerTransformedList <- list() # Power for(lagName in names(lagTrans)){ #lagName <- names(lagTrans)[1] if(as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 0 | as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 1){ alphaSteps <- 1 AlphaSeries <- as.numeric(as.character(olsm_varDetails$AlphaMin)) }else { alphaSteps <- (as.numeric(as.character(olsm_varDetails$AlphaMax))-as.numeric(as.character(olsm_varDetails$AlphaMin)))/(as.numeric(as.character(olsm_varDetails$AlphaSteps))-1) AlphaSeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) } lagTransPower <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])),n = length(AlphaSeries)),stringsAsFactors = F) names(AlphaSeries) <- paste0(lagName,"_P",seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) names(lagTransPower) <- names(AlphaSeries) lagTransPower <- as.data.frame.list(olsmgetPower(lagTransPower,AlphaSeries)) # Decay for(powerName in names(lagTransPower)){ # powerName <-names(lagTransPower)[1] if(as.numeric(as.character(olsm_varDetails$DecaySteps)) == 0 | as.numeric(as.character(olsm_varDetails$DecaySteps)) == 1){ decaySteps <- 1 decaySeries <- as.numeric(as.character(olsm_varDetails$DecayMin)) }else { decaySteps <- (as.numeric(as.character(olsm_varDetails$DecayMax))-as.numeric(as.character(olsm_varDetails$DecayMin)))/(as.numeric(as.character(olsm_varDetails$DecaySteps))-1) decaySeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) } lagTransPowerDecay <- as.data.frame(replicate(as.numeric(as.character(lagTransPower[,powerName])), n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(powerName,"_D",seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) names(lagTransPowerDecay) <- names(decaySeries) ## check for Transformation type, if it is Media Decay or Promo Decay. if(olsm_varDetails$Decay == "Media"){ lagTransPowerDecay <- olsmgetMediaDecay(lagTransPowerDecay,decaySeries) }else if(olsm_varDetails$Decay == "Promo"){ lagTransPowerDecay <- olsmgetPromoDecay(lagTransPowerDecay,decaySeries) } powerTransformedList <- c(powerTransformedList,lagTransPowerDecay) } } return(as.data.frame.list(powerTransformedList)) } #capturing Decay Power data olsmdecayPowerTrans <- function(olsm_RegDataTemp,lagTrans,name, olsm_varDetails){ powerTransformedList <- list() #Decay for(lagName in names(lagTrans)){ if(as.numeric(as.character(olsm_varDetails$DecaySteps)) == 0 | as.numeric(as.character(olsm_varDetails$DecaySteps)) == 1){ decaySteps <- 1 decaySeries <- as.numeric(as.character(olsm_varDetails$DecayMin)) }else { decaySteps <- (as.numeric(as.character(olsm_varDetails$DecayMax))-as.numeric(as.character(olsm_varDetails$DecayMin)))/(as.numeric(as.character(olsm_varDetails$DecaySteps))-1) decaySeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) } lagTransDecay <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])), n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(lagName,"_D",seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) names(lagTransDecay) <- names(decaySeries) ## condition to check if transformation is Media Decay or Promo Decay if(olsm_varDetails$Decay == "Media"){ lagTransDecay <- olsmgetMediaDecay(lagTransDecay,decaySeries) }else if(olsm_varDetails$Decay == "Promo"){ lagTransDecay <- olsmgetPromoDecay(lagTransDecay,decaySeries) } #Power for(decayName in names(lagTransDecay)){ if(as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 0 | as.numeric(as.character(olsm_varDetails$AlphaSteps)) == 1){ alphaSteps <- 1 AlphaSeries <- as.numeric(as.character(olsm_varDetails$AlphaMin)) }else { alphaSteps <- (as.numeric(as.character(olsm_varDetails$AlphaMax))-as.numeric(as.character(olsm_varDetails$AlphaMin)))/(as.numeric(as.character(olsm_varDetails$AlphaSteps))-1) AlphaSeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) } lagTransDecayPower <- as.data.frame(replicate(as.numeric(as.character(lagTransDecay[,decayName])),n = length(AlphaSeries)),stringsAsFactors = F) names(AlphaSeries) <- paste0(decayName,"_P",seq(from=as.numeric(as.character(olsm_varDetails$AlphaMin)),to=as.numeric(as.character(olsm_varDetails$AlphaMax)),by=alphaSteps)) names(lagTransDecayPower) <- names(AlphaSeries) lagTransDecayPower <- as.data.frame.list(olsmgetPower(lagTransDecayPower,AlphaSeries)) powerTransformedList <- c(powerTransformedList,lagTransDecayPower) } } return(as.data.frame.list(powerTransformedList)) } #capturing Decay data olsmDecayTrans <- function(olsm_RegDataTemp,lagTrans,name, olsm_varDetails){ decayTransformedList <- list() # Decay if(as.numeric(as.character(olsm_varDetails$DecaySteps)) == 0 | as.numeric(as.character(olsm_varDetails$DecaySteps)) == 1){ decaySteps <- 1 decaySeries <- as.numeric(as.character(olsm_varDetails$DecayMin)) }else { decaySteps <- (as.numeric(as.character(olsm_varDetails$DecayMax))-as.numeric(as.character(olsm_varDetails$DecayMin)))/(as.numeric(as.character(olsm_varDetails$DecaySteps))-1) decaySeries <- as.numeric(seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) } for(lagName in names(lagTrans)){ lagTransDecay <- as.data.frame(replicate(as.numeric(as.character(lagTrans[,lagName])), n = length(decaySeries)),stringsAsFactors = F) names(decaySeries) <- paste0(lagName,"_D",seq(from=as.numeric(as.character(olsm_varDetails$DecayMin)),to=as.numeric(as.character(olsm_varDetails$DecayMax)),by=decaySteps)) names(lagTransDecay) <- names(decaySeries) ## check for transformation, if it is Media Decay or Promo Decay. if(olsm_varDetails$Decay == "Media"){ lagTransDecay <- olsmgetMediaDecay(lagTransDecay,decaySeries) }else if(olsm_varDetails$Decay == "Promo"){ lagTransDecay <- olsmgetPromoDecay(lagTransDecay,decaySeries) } decayTransformedList <- c(decayTransformedList,lagTransDecay) } return(as.data.frame.list(decayTransformedList)) } createOlsmNormalization <- function(olsmFinalRegDf,olsm_parametersDF){ addGeo <- FALSE if(any(names(olsmFinalRegDf) %in% "Geography")){ addGeo <- TRUE geoName <- olsmFinalRegDf$Geography olsmFinalRegDf <- olsmFinalRegDf[,which(names(olsmFinalRegDf)!= "Geography")] } for(name in names(olsmFinalRegDf)[-1]){ if(as.character(olsm_parametersDF$Normalization[which(grepl(gsub("_L+[0-9].*","",name), olsm_parametersDF$VariableName))]) == "Division"){ #division if(mean(olsmFinalRegDf[,which(names(olsmFinalRegDf) == name)],na.rm = T)==0){ # divide by zero case handle olsmFinalRegDf[name]<-olsmFinalRegDf[name] }else{ olsmFinalRegDf[,which(names(olsmFinalRegDf) == name)] <- olsmFinalRegDf[,which(names(olsmFinalRegDf) == name)]/mean(olsmFinalRegDf[,which(names(olsmFinalRegDf) == name)],na.rm = T) } }else if(as.character(olsm_parametersDF$Normalization[which(grepl(gsub("_L+[0-9].*","",name), olsm_parametersDF$VariableName))]) == "Subtraction"){ #Subtraction olsmFinalRegDf[,which(names(olsmFinalRegDf) == name)] <- olsmFinalRegDf[,which(names(olsmFinalRegDf) == name)]-mean(olsmFinalRegDf[,which(names(olsmFinalRegDf) == name)],na.rm = T) }else{ # None olsmFinalRegDf[name]<-olsmFinalRegDf[name] } } if(addGeo == TRUE){ olsmFinalRegDf <- cbind("Geography" = geoName, olsmFinalRegDf) return(olsmFinalRegDf) }else{ return(olsmFinalRegDf[,-1]) } } ##################### Function related to Regression & Model Result ############ olsmGetFixedEffectDF <- function(olsmFinalRegDf, olsm_parametersDF, modelFeatureList){ olsmExcludeFixedEst <- function(df,depVar, fixedVarCoef){ for (i in 1:nrow(fixedVarCoef)) { df[,which(names(df) %in% fixedVarCoef$VariableName[i])] <- df[,which(names(df) %in% fixedVarCoef$VariableName[i])]* fixedVarCoef$Fixed_Coefficient[i] } fixedVar <- fixedVarCoef$VariableName if(length(fixedVar)==1){ df[,which(names(df)==depVar)] <- df[,which(names(df)==depVar)]- df[,which(names(df) %in% fixedVar)] }else{ df[,which(names(df)==depVar)] <- df[,which(names(df)==depVar)]- rowSums(df[,which(names(df) %in% fixedVar)]) } # removing fixed var from olsmFinalFixedRegDf df <- df[,-which(names(df) %in% fixedVar)] return(df) } df <- olsmFinalRegDf # copying the olsmFinalRegDf for making fixed df file. fixedVar <- olsm_parametersDF[grepl("Fixed Var",olsm_parametersDF$Type),1] if(length(fixedVar)!=0){ depVar <- olsm_parametersDF[grepl("DepVar",olsm_parametersDF$Type),1] # subtracting fixed df value from dependent variable. if(modelFeatureList$FixedVarChoice == "All"){ fixedVarCoef <- olsm_parametersDF[grepl("Fixed Var",olsm_parametersDF$Type),c("VariableName","Fixed_Coefficient")] nonFixedFinalRegDF <- olsmExcludeFixedEst(df, depVar, fixedVarCoef) }else if(modelFeatureList$FixedVarChoice == "Geo"){ fixedVarCoefByGeo <- split(modelFeatureList$geoFixedEstimatesDF,modelFeatureList$geoFixedEstimatesDF$Geography) geoData <- split(df, df$Geography) nonFixedFinalRegDF <- data.frame(rbindlist(lapply(names(fixedVarCoefByGeo), function(x){return(olsmExcludeFixedEst(geoData[[x]], depVar, fixedVarCoefByGeo[[x]]))})),row.names = NULL) } return(nonFixedFinalRegDF) }else { return(df) } } olsmCreateCombinedColumn <- function(df,combinedCol){ combinedColumns <- combinedCol[which(combinedCol$Combined_Column != 0),] if(nrow(combinedColumns)!=0){ columnsToBeDeleted <- combinedColumns$VariableName combinedColumnsList <<- split(combinedColumns$VariableName,combinedColumns$Combined_Column) combinedColumnsList <<- setNames(combinedColumnsList,paste0("Combined_",names(combinedColumnsList))) combinedColumnsdf <- do.call("cbind",lapply(combinedColumnsList,function(x){data.frame(rowSums(df[,x]))})) colnames(combinedColumnsdf) <- names(combinedColumnsList) finalCombined <- cbind(df,combinedColumnsdf) finalCombined <- finalCombined[ , !(names(finalCombined) %in% columnsToBeDeleted)] return(finalCombined) }else { return(df) } } olsmExtractOutsideVar <- function(fit, modelManager){ olsm_parametersDF <- modelManager modelVar <- names(fit$coefficients) outsideVar <- olsm_parametersDF$VariableName[grep("Outside",olsm_parametersDF$Type)] if(any(gsub("_L+[0-9].*","", modelVar) %in% outsideVar)){ return(as.character(modelVar[gsub("_L+[0-9].*","", modelVar) %in% outsideVar])) }else{ return("NO Outside (Base Model)") } } olsmGetOutsideTstatVIF <- function(model, outsideVar){ tstat <- 0 vifValue <- 0 if(any(gsub("_L+[0-9].*","",tidy(model)[,"term"]) %in% outsideVar)){ test <- as.data.frame(t(tidy(model)[-1]))[3,] colnames(test) = tidy(model)[,1] tstat <- round(test[,which(gsub("_L+[0-9].*","",colnames(test)) %in% outsideVar)],digits = 5) vifValue <- vif(model) vifValue <- vifValue[gsub("_L+[0-9].*","",names(vifValue)) %in% outsideVar] } return(t(data.frame(c(tstat,vifValue),row.names = NULL))) } #Rebuilding estimates for combined column by split olsmSplitCombinedEstimateData <- function(parameterDetails, parametersDf, transData){ index <- grep("Combined",parameterDetails$term) df <- parameterDetails[-index,] temp <- NULL combinedColumns <- parametersDf[which(parametersDf$Combined_Column != 0),] combinedColumnsList <- split(combinedColumns$VariableName,combinedColumns$Combined_Column) combinedColumnsList <- setNames(combinedColumnsList,paste0("Combined_",names(combinedColumnsList))) for (i in 1:length(index)) { # i = 3 combinedModelVar <- as.character(parameterDetails$term[index[i]]) combineVars <- combinedColumnsList[[combinedModelVar]] if(all(parametersDf$Normalization[parametersDf$VariableName %in% combineVars] != "None")){ combinedModelVarMean <- mean(rowSums(transData[,names(transData) %in% combineVars]),na.rm = T) combineVarsMeans <- colMeans(transData[,names(transData) %in% combineVars],na.rm = T) combinedVarRatio <- sapply(combineVarsMeans,FUN = function(x){if(x == 0){return(x)}else{return(x/combinedModelVarMean)}}) }else{ combinedVarRatio <- rep(1/length(combineVars), times = length(combineVars)) names(combinedVarRatio) <- combineVars } ####### Multiplying RolledEstimates with Number of Variables/length of combinedVarRatio [combined column] if(any(names(parameterDetails) %in% "Rolledup_Estimate")){ parameterDetails$Rolledup_Estimate[parameterDetails$term == parameterDetails$term[[index[i]]]] <- parameterDetails$Rolledup_Estimate[parameterDetails$term == parameterDetails$term[[index[i]]]] * length(combinedVarRatio) }else{ parameterDetails$estimate[parameterDetails$term == parameterDetails$term[[index[i]]]] <- parameterDetails$estimate[parameterDetails$term == parameterDetails$term[[index[i]]]] * length(combinedVarRatio) } temp <- parameterDetails[rep(index[i], each=length(combinedColumnsList[[as.character(parameterDetails$term[index[i]])]])),] temp$term <- combinedColumnsList[[as.character(parameterDetails$term[index[i]])]] for(var in names(combinedVarRatio)){ # var = names(combinedVarRatio)[1] if(any(names(temp) %in% "Rolledup_Estimate")){ temp$Rolledup_Estimate[temp$term == var] <- temp$Rolledup_Estimate[temp$term == var] * combinedVarRatio[names(combinedVarRatio) == var] }else{ temp$estimate[temp$term == var] <- temp$estimate[temp$term == var] * combinedVarRatio[names(combinedVarRatio) == var] } } names(temp) <- names(df) df <- data.frame(rbind(df, temp), row.names = NULL) } return(df) } olsmGetContribution <- function(olsmFullDecomp, depVar, unrolled){ # function for calculating contribution. # If olsmFullDecomp, depVar present with NULL unrolled then it will calculate for nonstacked model and appended up stacked model. # if unrolled is not NULL then contribution will be calculated by geography. if(is.null(unrolled)){ if(length(olsmFullDecomp)==2){ olsmFullDecomp <- olsmFullDecomp$FulldecompUnRolledDf }else{ olsmFullDecomp <- olsmFullDecomp$Fulldecomposition_BaseModel } } fullDecompAvg <- colMeans(olsmFullDecomp[,!names(olsmFullDecomp) %in% c("Period", "Geography",depVar)]) fullDecompAvg <- t(as.data.frame.list(fullDecompAvg/sum(fullDecompAvg)*100)) contributionDF <- data.frame(row.names(fullDecompAvg),fullDecompAvg, row.names = NULL) if(is.null(unrolled)){ names(contributionDF) <- c("term","Contribution%") }else{ names(contributionDF) <- c("term",unrolled) } return(contributionDF) } # New elasticity calculation need to build here by remove above function. Please Don't delete below commented section. olsmGetElasticity <- function(model,olsm_RegDataTemp,olsmModelFeatureList, olsm_parametersDF, olsmModelData, transData, actPredData){ # olsmModelFeatureList$elasticityValue <- 1 olsmGetElastctContribution <- function(model,olsmElasticRegData, olsmModelFeatureList, olsm_parametersDF, olsmModelData,olsm_RegDataTemp, varMean, transData){ olsmElasticRegData$Geography <- as.character(olsmElasticRegData$Geography) olsm_SplitElasticAF <- split(olsmElasticRegData, olsmElasticRegData$Geography) olsm_splitByGeoSubset <- olsm_SplitElasticAF[which(names(olsm_SplitElasticAF) %in% olsmModelFeatureList$selectedGeos)] olsmTransElasticAF <- NULL for (name in names(olsm_splitByGeoSubset)) { #name = names(olsm_splitByGeoSubset)[1] olsmModelFeatureList[["TransGeo"]] <- name df <- data.frame(createOlsmTransformation(olsm_RegDataModelList = olsm_splitByGeoSubset, olsm_parametersDF, modelFeatureList = olsmModelFeatureList),stringsAsFactors = FALSE) df <- df[dmy(df$Period) %in% dmy(olsmModelFeatureList$modellingPeriod),] olsmTransElasticAF[[name]] <- df } olsmTransElasticAF <- as.data.frame(rbindlist(olsmTransElasticAF)) olsmTransElasticAF$Period <- lubridate::dmy(olsmTransElasticAF$Period) dummyVarName <- NULL if(olsmModelFeatureList$DummyFlag == TRUE){ olsmTransElasticAF <- olsmTransElasticAF[olsmTransElasticAF$Period %in% olsmModelData$Period,] dummyVarName <- grep("Dummy_Var+[0-9]",names(olsmModelData),value = T) dummyVar <- olsmModelData[,grep("Dummy_Var+[0-9]",names(olsmModelData))] tmp <- cbind(olsmTransElasticAF, dummyVarName = dummyVar + (dummyVar*olsmModelFeatureList$elasticityValue/100)) names(tmp) <- c(names(olsmTransElasticAF),dummyVarName) olsmTransElasticAF <- tmp } #olsmFullDecompList <- olsmExtractFullDecomp(model, olsm_parametersDF, olsmModelData = olsmTransElasticAF,modelFeatureList = olsmModelFeatureList, olsm_RegDataTemp, transData) olsmFullDecompList <- olsmExtractFullDecomp(model, olsm_parametersDF, olsmModelData = olsmTransElasticAF,modelFeatureList = olsmModelFeatureList, olsm_RegDataTemp, transData) olsmElasticFullDecomp <- list() olsmElasticFullDecomp[["FulldecompUnRolledDf"]] <- as.data.frame(rbindlist(olsmFullDecompList)) #olsmElasticFullDecomp[["FulldecompUnRolledDf"]] <- as.data.frame(rbindlist(lapply(olsmFullDecompList, function(x) olsmMinMaxAdjust(x, olsmModelFeatureList)))) olsmElasticFullDecomp$FulldecompUnRolledDf$Period <- as.character(olsmElasticFullDecomp$FulldecompUnRolledDf$Period) olsmElasticFullDecompList <- split(olsmElasticFullDecomp$FulldecompUnRolledDf, olsmElasticFullDecomp$FulldecompUnRolledDf$Geography) olsmElasticFullDecomp <- lapply(names(olsmElasticFullDecompList), function(x){ sapply(names(olsmElasticFullDecompList[[x]]),function(y){ if(any(y %in% c("Geography","Period", "Intercept",dummyVarName))){ olsmElasticFullDecompList[[x]][,names(olsmElasticFullDecompList[[x]]) %in% y] }else{ if(olsm_parametersDF$Normalization[olsm_parametersDF$VariableName %in% gsub("_L+[0-9].*", "", y)] %in% c("Division")){ olsmElasticFullDecompList[[x]][,names(olsmElasticFullDecompList[[x]]) %in% y]/as.numeric(varMean[which(varMean$Geography ==x), names(varMean) %in% y]) }else if(olsm_parametersDF$Normalization[olsm_parametersDF$VariableName %in% gsub("_L+[0-9].*", "", y)] %in% c("Subtraction")){ olsmElasticFullDecompList[[x]][,names(olsmElasticFullDecompList[[x]]) %in% y]-as.numeric(varMean[which(varMean$Geography ==x), names(varMean) %in% y]) }else if(olsm_parametersDF$Normalization[olsm_parametersDF$VariableName %in% gsub("_L+[0-9].*", "", y)] %in% c("None")){ olsmElasticFullDecompList[[x]][,names(olsmElasticFullDecompList[[x]]) %in% y] } } }) }) # olsmElasticFullDecompList$Total$Geography <- as.character(olsmElasticFullDecompList$Total$Geography) olsmElasticFullDecomp <- as.data.frame(rbindlist(lapply(olsmElasticFullDecomp, function(x) data.frame(x,stringsAsFactors = F)))) return(olsmElasticFullDecomp) } olsmElasticRegData <- olsm_RegDataTemp olsmElasticRegData$Geography <- as.character(olsmElasticRegData$Geography) olsmFullDecompList <- olsmExtractFullDecomp(model, olsm_parametersDF, olsmModelData,modelFeatureList = olsmModelFeatureList, olsm_RegDataTemp, transData) olsmFullDecomp <- list() olsmFullDecomp[["FulldecompUnRolledDf"]] <- as.data.frame(rbindlist(olsmFullDecompList)) modelData <- transData geoDepMean <- modelData[modelData$Geography %in% olsmModelFeatureList$selectedGeos,] varMean <- aggregate(geoDepMean[,!names(geoDepMean) %in% c("Geography","Period")], by = list(geoDepMean$Geography), FUN=mean) names(varMean)[which(names(varMean) == "Group.1")] <- "Geography" # calculating elasticity of all full data points baseFullDecomp <- olsmFullDecomp$FulldecompUnRolledDf[,!names(olsmFullDecomp$FulldecompUnRolledDf) %in% c("Geography", "Period")] olsmElasticFullDecomp <- olsmGetElastctContribution(model,olsmElasticRegData, olsmModelFeatureList, olsm_parametersDF, olsmModelData,olsm_RegDataTemp, varMean, transData) elasticFullDecomp <- as.data.frame(apply(olsmElasticFullDecomp [,!names(olsmElasticFullDecomp) %in% c("Geography","Period")], 2, as.numeric)) Elasticity_Modelling_Period <- (colMeans(elasticFullDecomp - baseFullDecomp)/mean(actPredData$Predicted))*100 ## Calculation of Elasticity by Geography... olsmElasticFullDecomp split by geo olsmGeoElasticFullDecomp <- split(olsmElasticFullDecomp,olsmElasticFullDecomp$Geography) # olsmFullDecomp$FulldecompUnRolledDf split by geo OlsmFullDecompUnRolledGeoSplit <- split(olsmFullDecomp$FulldecompUnRolledDf,olsmFullDecomp$FulldecompUnRolledDf$Geography) #actualVsPrediacted Geo Split GeoSplitActPredData <- split(actPredData,actPredData$Geography) ElasticityByGeography <- data.frame() ## Elasticity by Geography for(name in names(olsmGeoElasticFullDecomp)){ # name = names(olsmGeoElasticFullDecomp)[1] GeoSplitbaseFullDecomp <- OlsmFullDecompUnRolledGeoSplit[[name]][,!names(olsmFullDecomp$FulldecompUnRolledDf) %in% c("Geography", "Period")] GeoSplitElasticFullDecomp <- as.data.frame(apply(olsmGeoElasticFullDecomp[[name]] [,!names(olsmGeoElasticFullDecomp[[name]]) %in% c("Geography","Period")], 2, as.numeric)) ## Elasticity after Geo Split GoeSplit_Elasticity_Modelling_Priod <- (colMeans(GeoSplitElasticFullDecomp - GeoSplitbaseFullDecomp)/mean(GeoSplitActPredData[[name]]$Predicted))*100 GoeSplit_Elasticity_Modelling_Priod <- data.frame(t(data.frame(GoeSplit_Elasticity_Modelling_Priod)),row.names = NULL) GoeSplit_Elasticity_Modelling_Priod <- cbind(Geography = name, GoeSplit_Elasticity_Modelling_Priod) ElasticityByGeography <- rbind(ElasticityByGeography, GoeSplit_Elasticity_Modelling_Priod) } # calculating elasticity of 12 months data points olsmModelFeatureList$elasticityL12Flag <- TRUE olsmElasticFullDecomp <- olsmGetElastctContribution(model,olsmElasticRegData, olsmModelFeatureList, olsm_parametersDF, olsmModelData,olsm_RegDataTemp, varMean, transData) olsmFullDecomp$FulldecompUnRolledDf$Period <- zoo::as.yearmon(olsmFullDecomp$FulldecompUnRolledDf$Period) olsmElasticFullDecomp$Period <- zoo::as.yearmon(as.Date(olsmElasticFullDecomp$Period)) baseFullDecomp_12 <- olsmFullDecomp$FulldecompUnRolledDf[olsmFullDecomp$FulldecompUnRolledDf$Period %in% tail(unique(olsmFullDecomp$FulldecompUnRolledDf$Period),n = 12),!names(olsmFullDecomp$FulldecompUnRolledDf) %in% c("Geography", "Period")] elasticFullDecomp_12 <- apply(olsmElasticFullDecomp [olsmElasticFullDecomp$Period %in% tail(unique(olsmElasticFullDecomp$Period),n = 12),!names(olsmElasticFullDecomp) %in% c("Geography", "Period")],2,as.numeric) Elasticity_L12_Modelling_Period <- (colMeans(elasticFullDecomp_12 - baseFullDecomp_12)/mean(actPredData$Predicted[olsmFullDecomp$FulldecompUnRolledDf$Period %in% tail(unique(olsmFullDecomp$FulldecompUnRolledDf$Period),n = 12)]))*100 ## Elasticity by Geography of last 12 months # olsmElasticFullDecomp split by geo olsmGeoElasticFullDecomp <- split(olsmElasticFullDecomp,olsmElasticFullDecomp$Geography) # olsmFullDecomp$FulldecompUnRolledDf split by geo after conversion of period to month year format. OlsmFullDecompUnRolledGeoSplit <- split(olsmFullDecomp$FulldecompUnRolledDf,olsmFullDecomp$FulldecompUnRolledDf$Geography) ## Elasticity by Geography ElasticityL12ByGeography <- data.frame() for(name in names(olsmGeoElasticFullDecomp)){ # name = names(olsmGeoElasticFullDecomp)[1] baseFullDecomp_12_GeoSplit <- OlsmFullDecompUnRolledGeoSplit[[name]][OlsmFullDecompUnRolledGeoSplit[[name]]$Period %in% tail(unique(olsmFullDecomp$FulldecompUnRolledDf$Period),n = 12),!names(olsmFullDecomp$FulldecompUnRolledDf) %in% c("Geography", "Period")] #write.csv(baseFullDecomp_12_GeoSplit, file = "baseFullDecomp_12_GeoSplit.csv", row.names = F) elasticFullDecomp_12_GeoSplit <- apply(olsmGeoElasticFullDecomp[[name]] [olsmGeoElasticFullDecomp[[name]]$Period %in% tail(unique(olsmGeoElasticFullDecomp[[name]]$Period),n = 12),!names(olsmGeoElasticFullDecomp[[name]]) %in% c("Geography", "Period")],2,as.numeric) #write.csv(elasticFullDecomp_12_GeoSplit, file = "elasticFullDecomp_12_GeoSplit.csv", row.names = F) ## Elasticity after Geo Split Elasticity_L12_Modelling_Period_GeoSplit <- (colMeans(elasticFullDecomp_12_GeoSplit - baseFullDecomp_12_GeoSplit)/mean(GeoSplitActPredData[[name]]$Predicted[OlsmFullDecompUnRolledGeoSplit[[name]]$Period %in% tail(unique(olsmFullDecomp$FulldecompUnRolledDf$Period),n = 12)]))*100 Elasticity_L12_Modelling_Period_GeoSplit <- data.frame(t(data.frame(Elasticity_L12_Modelling_Period_GeoSplit)),row.names = NULL) Elasticity_L12_Modelling_Period_GeoSplit <- cbind(Geography = name, Elasticity_L12_Modelling_Period_GeoSplit) ElasticityL12ByGeography <- rbind(ElasticityL12ByGeography, Elasticity_L12_Modelling_Period_GeoSplit) } Elasticity_L12_Modelling_Period["Intercept"] <- Elasticity_Modelling_Period["Intercept"] <- ElasticityL12ByGeography["Intercept"] <- ElasticityByGeography["Intercept"] <- 0 ElasticityFullPeriod_L12 <- data.frame(term = names(Elasticity_Modelling_Period),Elasticity_Modelling_Period, Elasticity_L12_Modelling_Period,row.names = NULL) Calculated_Elasticity <- list() Calculated_Elasticity[["ElasticityByGeography"]] <- data.frame(ElasticityByGeography[,c(which(names(ElasticityByGeography) == "Geography"),which(names(ElasticityByGeography) != "Geography"))],row.names = NULL) Calculated_Elasticity[["ElasticityL12ByGeography"]] <- data.frame(ElasticityL12ByGeography[,c(which(names(ElasticityL12ByGeography) == "Geography"),which(names(ElasticityL12ByGeography) != "Geography"))],row.names = NULL) Calculated_Elasticity[["ElasticityFullPeriod_L12"]] <- ElasticityFullPeriod_L12 Calculated_Elasticity <- lapply(Calculated_Elasticity, function(x){ x[is.na(x)]<- 0 return(x) }) Calculated_Elasticity$ElasticityByGeography[,which(names(Calculated_Elasticity$ElasticityByGeography) %in% olsmModelFeatureList$depVar)] <- NULL Calculated_Elasticity$ElasticityL12ByGeography[,which(names(Calculated_Elasticity$ElasticityL12ByGeography) %in% olsmModelFeatureList$depVar)] <- NULL Calculated_Elasticity$ElasticityFullPeriod_L12[which(Calculated_Elasticity$ElasticityFullPeriod_L12$term == olsmModelFeatureList$depVar),-1] <- NA return(Calculated_Elasticity) } olsmGetModelParameter <- function(model, olsm_parametersDF,olsmModelData, olsmModelFeatureList, olsm_RegDataTemp, transData){ depVar <- olsm_parametersDF$VariableName[olsm_parametersDF$Type=="DepVar"] # getting VIF of variable VIF <- data.frame(term = names(rms::vif(model)), VIF = rms::vif(model),row.names = NULL) # checking for combined column, if present then split the value of combined columns. if(any(grepl("Combined",names(model$coefficients)))){ modelParam <- merge(tidy(model),VIF,by = "term",all = T) parameterDetails <- olsmSplitCombinedEstimateData(parameterDetails = modelParam, parametersDf = olsm_parametersDF, transData) }else{ parameterDetails <- merge(tidy(model),VIF,by = "term",all = T) } # checking for fixed estimates, if present in model manager, then add to the model parameterDetails if(any(grepl("Fixed",olsm_parametersDF$Type))){ if(olsmModelFeatureList$FixedVarChoice == "All"){ fixedVar <- cbind(olsm_parametersDF[grep("Fixed",olsm_parametersDF$Type),c("VariableName","Fixed_Coefficient")],matrix(NA,length(grep("Fixed",olsm_parametersDF$Type)),length(parameterDetails)-2)) names(fixedVar) <- names(parameterDetails) parameterDetails <- rbind(parameterDetails,fixedVar) }else if(olsmModelFeatureList$FixedVarChoice == "Geo"){ fixedGeoDf <- olsmModelFeatureList$geoFixedEstimatesDF[,c("VariableName","Fixed_Coefficient")] fixedGeoDf <- data.frame(aggregate(fixedGeoDf$Fixed_Coefficient,by = list(fixedGeoDf$VariableName),FUN = mean,na.rm = T), matrix(NA,length(grep("Fixed",olsm_parametersDF$Type)),length(parameterDetails)-2)) names(fixedGeoDf) <- names(parameterDetails) parameterDetails <- rbind(parameterDetails,fixedGeoDf) } } # checking for multicollinearity, if present then adding multicollinear variable with 0 estimate and NA in other columns. if(nrow(tidy(model)) != length(names(model$coefficients))){ # here is handling multicollinearity in model result. multiCorVar <- names(model$coefficients)[!names(model$coefficients) %in% tidy(model)$term] multiCorVar <- data.frame(multiCorVar,matrix(0, nrow = length(multiCorVar), ncol = 1),matrix(NA, nrow = length(multiCorVar), ncol = length(parameterDetails)-2)) names(multiCorVar) <- names(parameterDetails) parameterDetails <- rbind(parameterDetails, multiCorVar) } # reordering parameterDetails as per model manager varRowName <- parameterDetails$term varOrderMM <- olsm_parametersDF$VariableName[olsm_parametersDF$Type != "Not in Model"] if(any(varRowName %in% "(Intercept)")){ varRowName[varRowName %in% "(Intercept)"] <- depVar } rownames(parameterDetails) <- as.character(sapply(varRowName, function(x) gsub("_L+[0-9].*","",x))) varOrderMM <- varOrderMM[varOrderMM %in% row.names(parameterDetails)] olsmResult <- data.frame(parameterDetails[order(match(row.names(parameterDetails),varOrderMM)),],row.names = NULL) olsmResult$term[grep("Intercept",olsmResult$term)] <- gsub("\$|\$","",olsmResult$term[grep("Intercept",olsmResult$term)]) #olsmFullDecomp <- olsmExtractFullDecomp(model, olsm_parametersDF, olsmModelData,olsmModelFeatureList, olsm_RegDataTemp, transData) olsmFullDecompList <- olsmExtractFullDecomp(model, olsm_parametersDF, olsmModelData,olsmModelFeatureList, olsm_RegDataTemp, transData) if(length(olsmModelFeatureList$min_max_var$VariableName) > 0){ olsmModelFeatureList[["fullDecompMinMaxAdjustDF"]] <- olsmExtractBaseFullDecompMinMax(olsmFullDecompList, olsmModelFeatureList) } olsmFullDecomp <- list() olsmFullDecomp[["FulldecompUnRolledDf"]] <- as.data.frame(rbindlist(lapply(olsmFullDecompList, function(x)olsmMinMaxAdjust(x, olsmModelFeatureList)))) olsmFullDecomp[["FulldecompRolledDf"]] <- olsmExtractFullDecompRolledUp(olsmFullDecomp[["FulldecompUnRolledDf"]]) modelContribution <- olsmGetContribution(olsmFullDecomp, olsmModelFeatureList$depVar, unrolled = NULL) olsmResult <- merge(olsmResult, modelContribution, by = "term",sort = F,all = T) return(olsmResult) } # Function to extract model parameters to display on screen olsmExtractModelParameter <- function(olsmAllModelList, olsm_parametersDF){ # calculating number of models n.models <- length(olsmAllModelList) if(n.models == 0){ # if there is no model, then message will display to change parameter. return(0) }else{ # calling the function to extract the parameter of each model to rank. result <- lapply(olsmAllModelList, olsmExtractModelParameterValue) result <- as.data.frame(matrix(unlist(result), nrow=n.models, byrow=T)) modelOutside <- unlist(lapply(olsmAllModelList, function(x, modelManager) olsmExtractOutsideVar(x,modelManager = olsm_parametersDF),modelManager=olsm_parametersDF)) olsmModelResult <- cbind(paste0("Model_",1:nrow(result)), modelOutside, result) rownames(olsmModelResult) <- NULL colnames(olsmModelResult) <- c("Model_No", "Outside_Variable","%R2","%R2.adj","DW","RootMSE") olsmModelResult$`%R2` <- sapply(olsmModelResult$`%R2`, function(x) x <- round((x * 100),digits = 2)) olsmModelResult$`%R2.adj` <- sapply(olsmModelResult$`%R2.adj`, function(x) x <- round((x * 100),digits = 2)) return(olsmModelResult) } } # fucntion to extract model parameter to rank the model olsmExtractModelParameterValue <- function(fit) { R2 <- summary(fit)$r.squared R2.adj <- summary(fit)$adj.r.squared dw <- durbinWatsonTest(fit)[[2]] RootMSE <- sqrt(mean(fit$residuals^2)) out <- data.frame(R2=R2, R2.adj=R2.adj,DurbinWatson=dw, RootMSE = RootMSE) out <- sapply(out,function(x) if(!is.nan(x)) {x <- x} else{x <- 0} ) return(out) } olsm_getActualVsPredictedDf <- function(model, olsm_parametersDF, regDf, modelFeatureList){ olsmIncludeFixedEst <- function(df, actPredData, predictedVar, fixedVarCoef){ for (i in 1:nrow(fixedVarCoef)) { df[,which(names(df) %in% fixedVarCoef$VariableName[i])] <- df[,which(names(df) %in% fixedVarCoef$VariableName[i])] * fixedVarCoef$Fixed_Coefficient[i] } fixedVar <- fixedVarCoef$VariableName if(length(fixedVar)==1){ actPredData[,which(names(actPredData)==predictedVar)] <- actPredData[,which(names(actPredData)==predictedVar)] + df[,which(names(df) %in% fixedVar)] }else{ actPredData[,which(names(actPredData)==predictedVar)] <- actPredData[,which(names(actPredData)==predictedVar)] + rowSums(df[,which(names(df) %in% fixedVar)]) } return(actPredData) } data <- regDf olsm_actPred <- cbind.data.frame("Geography" = data[,"Geography"],Period =data[,"Period"], Actual = data[,olsm_parametersDF$VariableName[olsm_parametersDF$Type == "DepVar"]], Predicted = fitted(model), Residual = residuals(model)) # code to add fixed effect value to predData. if(any(grepl("Fixed",olsm_parametersDF$Type))){ if(modelFeatureList$FixedVarChoice == "All"){ fixedVarCoef <- olsm_parametersDF[grepl("Fixed Var",olsm_parametersDF$Type),c("VariableName","Fixed_Coefficient")] olsm_actPred <- olsmIncludeFixedEst(df = data ,actPredData = olsm_actPred, predictedVar = "Predicted", fixedVarCoef) }else if(modelFeatureList$FixedVarChoice == "Geo"){ fixedVarCoefByGeo <- split(modelFeatureList$geoFixedEstimatesDF,modelFeatureList$geoFixedEstimatesDF$Geography) geoData <- split(data, data$Geography) olsm_actPred <- split(olsm_actPred, olsm_actPred$Geography) olsm_actPred <- data.frame(rbindlist(lapply(names(fixedVarCoefByGeo), function(x){ return(olsmIncludeFixedEst(df = geoData[[x]] ,actPredData = olsm_actPred[[x]], predictedVar = "Predicted", fixedVarCoefByGeo[[x]])) })),row.names = NULL) } } return(olsm_actPred) } olsmDenormModelEstimate <- function(ModelDf, olsm_parametersDF, olsmFinalTransRegDf, olsmModelFeatureList){ if(any(grepl("Combined",ModelDf$term))){ combinedDf <- olsmSplitCombinedEstimateData(ModelDf, olsm_parametersDF, olsmFinalTransRegDf) ModelDf <- data.frame(combinedDf[,which(names(combinedDf) %in% c("term","estimate"))],row.names = NULL) } if(any(grepl("Fixed",olsm_parametersDF$Type))){ fixedDf <- data.frame(olsm_parametersDF[grep("Fixed",olsm_parametersDF$Type),names(olsm_parametersDF) %in% c("VariableName","Fixed_Coefficient")],row.names = NULL) names(fixedDf) <- c("term","estimate") fixedVarDf <- data.frame(fixedDf$term, fixedDf$estimate, matrix(NA, nrow = nrow(fixedDf), ncol = ncol(ModelDf)-ncol(fixedDf))) names(fixedVarDf) <- names(ModelDf) ModelDf <- rbind(ModelDf,fixedVarDf) } olsmSplittedTransRegDf <- split(olsmFinalTransRegDf[names(olsmFinalTransRegDf) %in% c("Geography", olsmModelFeatureList$depVar,ModelDf$term)], olsmFinalTransRegDf$Geography) ModelGeoVarAvg <- data.frame(cbind(Geography= names(olsmSplittedTransRegDf),rbindlist(lapply(names(olsmSplittedTransRegDf), function(x){data.frame(t(colMeans(olsmSplittedTransRegDf[[x]][,-1],na.rm = T)))})))) DenormModelEst <- data.frame(Geography = names(olsmSplittedTransRegDf), matrix(ncol = length(ModelDf$term),nrow = length(names(olsmSplittedTransRegDf))), stringsAsFactors = F) names(DenormModelEst)[-1] <- ModelDf$term if(olsm_parametersDF$Normalization[olsm_parametersDF$Type == "DepVar"] == "Division"){ for(name in ModelDf$term){ # name = ModelDf$term[4] varName <- gsub("_L+[0-9].*","",name) if(varName == "(Intercept)"){ DenormModelEst[,name] <- ModelDf$estimate[ModelDf$term == name] * ModelGeoVarAvg[,olsmModelFeatureList$depVar] }else if(grepl("Dummy_Var",name)){ DenormModelEst[,name] <- ModelDf$estimate[ModelDf$term == name] }else{ if(olsm_parametersDF$Normalization[olsm_parametersDF$VariableName == varName] == "Division"){ varAvg <- ModelGeoVarAvg[,olsmModelFeatureList$depVar]/ModelGeoVarAvg[,name] varAvg[is.infinite(varAvg)] <- 0 DenormModelEst[,name] <- ModelDf$estimate[ModelDf$term == name] * varAvg }else if(olsm_parametersDF$Normalization[olsm_parametersDF$VariableName == varName] != "Division"){ DenormModelEst[,name] <- ModelDf$estimate[ModelDf$term == name] } } } }else if(olsm_parametersDF$Normalization[olsm_parametersDF$Type == "DepVar"] != "Division"){ for(name in ModelDf$term){ varName <- gsub("_L+[0-9].*","",name) if(name == "(Intercept)" | grepl("Dummy_Var",name)){ DenormModelEst[,name] <- ModelDf$estimate[ModelDf$term == name] }else{ if(olsm_parametersDF$Normalization[olsm_parametersDF$VariableName == varName] == "Division"){ DenormModelEst[,name] <- ModelDf$estimate[ModelDf$term == name]/ModelGeoVarAvg[,name] }else if(olsm_parametersDF$Normalization[olsm_parametersDF$VariableName == varName] != "Division"){ DenormModelEst[,name] <- ModelDf$estimate[ModelDf$term == name] } } } } return(DenormModelEst) } olsm_MixedModelDenormEstimate <- function(olsm_parametersDF,unrolledEstimate,VarMeanGeoData){ depVar <- olsm_parametersDF$VariableName[olsm_parametersDF$Type == "DepVar"] geo <- names(unrolledEstimate)[!names(unrolledEstimate) %in% "term"] for(name in geo){ if(olsm_parametersDF$Normalization[olsm_parametersDF$Type == "DepVar"] == "Division"){ depAvg <- VarMeanGeoData[VarMeanGeoData$Geography == name,which(names(VarMeanGeoData) == depVar)] unrolledEstimate[,name] <- unrolledEstimate[,name] * depAvg }else { indepVar <- as.character(unrolledEstimate$term[!unrolledEstimate$term %in% "(Intercept)"]) for(indepname in indepVar){ if(olsm_parametersDF$Normalization[olsm_parametersDF$VariableName == indepname] == "Division"){ unrolledEstimate[unrolledEstimate$term == indepname,name] <- unrolledEstimate[unrolledEstimate$term == indepname,name]/VarMeanGeoData[VarMeanGeoData$Geography == name,indepname] } } } } return(unrolledEstimate) } ##################### Function related to Model Result Download ################ olsmExtractModelDetail <- function(model, modelResult,olsm_parametersDF, obsCount,olsmModelFeatureList){ output <- NULL output <- c(output,"The REG Procedure") output <- c(output,"\n\n") output <- c(output,paste("Model:",modelResult[1,1])) output <- c(output,paste("Dependant Variable:",names(model$model[1]))) output <- c(output,"\n\n") output <- c(output,paste("Number of Observations Used in Model:",obsCount)) output <- c(output,"\n\n") output <- c(output,paste("AF Start Date:",olsmModelFeatureList$startDate)) output <- c(output,"\n\n") output <- c(output,paste("AF End Date:",olsmModelFeatureList$endDate)) output <- c(output,"\n\n") output <- c(output,paste("Model Start Date:",min(lubridate::dmy(olsmModelFeatureList$modellingPeriod)))) output <- c(output,"\n\n") output <- c(output,paste("Model End Date:",max(lubridate::dmy(olsmModelFeatureList$modellingPeriod)))) output <- c(output,"\n\n") output <- c(output,paste("Adstock Choice:",olsmModelFeatureList$adStockChoice)) output <- c(output,"\n\n") output <- c(output,paste("Intercept:",olsmModelFeatureList$hasIntercept)) output <- c(output,"\n\n") output <- c(output,paste("Weight:",olsmModelFeatureList$wLSChoice)) output <- c(output,"\n\n") output <- c(output,paste("Intercept:",olsmModelFeatureList$hasIntercept)) output <- c(output,"\n\n") output <- c(output,paste("Mixed Model Chioce:",olsmModelFeatureList$mixedModelChioce)) output <- c(output,"\n\n") output <- c(output,noquote(capture.output(write.csv(modelResult,stdout(),row.names = F,quote = F)))) output <- c(output,"\n\n") # output <- c(output,noquote(capture.output(write.csv(olsmResult,file = stdout(),row.names = F,quote = F)))) # output <- c(output,"\n\n") return(output) } olsmExtractMixedModel <- function(model,olsmFinalNormRegDf,modelFeatureList,olsm_parametersDF,olsm_RegDataTemp, olsmFinalTransRegDf){ # Model Features modelFeature <- data.frame(Method = model$method,AIC = summary(model)["AIC"],BIC = summary(model)["BIC"],logLik = -2*model$logLik) names(modelFeature)[names(modelFeature) %in% "logLik"] <- "-2logLik" # Rolled Up Mixed Model Result rolledEstimate <- data.frame(summary(model)$tTable)[c("Value","Std.Error","t.value","p.value")] names(rolledEstimate) <- c("Rolledup_Estimate","Rolledup_Std.Error","Rolledup_t.value","Rolledup_p.value") rolledEstimate <- data.frame(term = rownames(rolledEstimate), rolledEstimate, row.names = NULL,stringsAsFactors = F) if(any(grepl("Combined",rolledEstimate$term))){ rolledEstimate <- data.frame(olsmSplitCombinedEstimateData(rolledEstimate, olsm_parametersDF, olsmFinalTransRegDf),row.names = NULL) } if(any(grepl("(Intercept)",rolledEstimate$term))){ rolledEstimate$term[grep("(Intercept)",rolledEstimate$term)] <- "Intercept" } if(any(grepl("Fixed",olsm_parametersDF$Type))){ if(modelFeatureList$FixedVarChoice == "All"){ fixedDf <- data.frame(olsm_parametersDF[grep("Fixed",olsm_parametersDF$Type),names(olsm_parametersDF) %in% c("VariableName","Fixed_Coefficient")],row.names = NULL) fixedDf <- data.frame(fixedDf, matrix(NA,nrow = length(fixedDf),ncol = length(rolledEstimate)-2)) names(fixedDf) <- names(rolledEstimate) rolledEstimate <- rbind(rolledEstimate,fixedDf) }else if(modelFeatureList$FixedVarChoice == "Geo"){ fixedDf <- olsm_parametersDF[grep("Fixed",olsm_parametersDF$Type),names(olsm_parametersDF) %in% "VariableName"] fixedGeoDf <- modelFeatureList$geoFixedEstimatesDF[,c("VariableName","Fixed_Coefficient")] fixedGeoDf <- data.frame(aggregate(fixedGeoDf$Fixed_Coefficient,by = list(fixedGeoDf$VariableName),FUN = mean,na.rm = T), matrix(NA,nrow = length(fixedDf),ncol = length(rolledEstimate)-2)) names(fixedGeoDf) <- names(rolledEstimate) rolledEstimate <- rbind(rolledEstimate,fixedGeoDf) } } ## calculating Contribution olsmModelData <- olsmFinalNormRegDf olsmModelData$Period <- lubridate::dmy(olsmModelData$Period) olsmFullDecompList <- olsmExtractFullDecomp(model, olsm_parametersDF, olsmModelData,modelFeatureList, olsm_RegDataTemp, olsmFinalTransRegDf) if(length(modelFeatureList$min_max_var$VariableName) > 0){ modelFeatureList[["fullDecompMinMaxAdjustDF"]] <- olsmExtractBaseFullDecompMinMax(olsmFullDecompList, modelFeatureList) } olsmFullDecomp <- list() olsmFullDecomp[["FulldecompUnRolledDf"]] <- as.data.frame(rbindlist(lapply(olsmFullDecompList, function(x)olsmMinMaxAdjust(x, modelFeatureList)))) olsmFullDecomp[["FulldecompRolledDf"]] <- olsmExtractFullDecompRolledUp(olsmFullDecomp[["FulldecompUnRolledDf"]]) rolledEstimate <- merge(rolledEstimate,olsmGetContribution(olsmFullDecomp, modelFeatureList$depVar, unrolled = NULL),by = "term",sort = F) # Unrolled Up Mixed Model Result unrolledEstimate <- data.frame(term = rownames(t(coef(model))),data.frame(t(coef(model)),row.names = NULL)) if(any(grepl("Combined",unrolledEstimate$term))){ modelDFList <- list() modelDFList <- lapply(as.list(unrolledEstimate[-1]), function(x){data.frame(unrolledEstimate[1],estimate = x, stringsAsFactors = F)}) geoTransData <- split(olsmFinalTransRegDf, olsmFinalTransRegDf$Geography) modelDFList <- lapply(names(modelDFList), function(x){return(olsmSplitCombinedEstimateData(modelDFList[[x]], olsm_parametersDF, geoTransData[[x]]))}) unrolledEstimate <- data.frame(modelDFList[[1]][,1],do.call(cbind, lapply(modelDFList, function(df) df$estimate)), row.names = NULL) names(unrolledEstimate) <- c("term", names(geoTransData)) } unrolledEstimate$term <- as.character(unrolledEstimate$term) if(any(grepl("(Intercept)",unrolledEstimate$term))){ unrolledEstimate$term[grep("(Intercept)",unrolledEstimate$term)] <- "Intercept" } if(any(grepl("Fixed",olsm_parametersDF$Type))){ if(modelFeatureList$FixedVarChoice == "All"){ fixedDf <- data.frame(olsm_parametersDF[grep("Fixed",olsm_parametersDF$Type),names(olsm_parametersDF) %in% c("VariableName","Fixed_Coefficient")],row.names = NULL) fixedDf <- data.frame(fixedDf, matrix(fixedDf$Fixed_Coefficient, nrow = nrow(fixedDf), ncol = ncol(unrolledEstimate)-2)) names(fixedDf) <- names(unrolledEstimate) unrolledEstimate <- rbind(unrolledEstimate,fixedDf) }else if(modelFeatureList$FixedVarChoice == "Geo"){ fixedDf <- olsm_parametersDF[grep("Fixed",olsm_parametersDF$Type),names(olsm_parametersDF) %in% "VariableName"] fixedGeoDf <- modelFeatureList$geoFixedEstimatesDF[,c("Geography","VariableName","Fixed_Coefficient")] fixedGeoDf <- reshape2::dcast(fixedGeoDf,formula = VariableName ~ Geography,value.var = "Fixed_Coefficient") names(fixedGeoDf) <- names(unrolledEstimate) unrolledEstimate <- rbind(unrolledEstimate,fixedGeoDf) } } # Unrolled Contribution by Geography olsmFullDecompList <- split(olsmFullDecomp$FulldecompUnRolledDf,olsmFullDecomp$FulldecompUnRolledDf$Geography) geoContrList <- lapply(names(olsmFullDecompList), function(x) olsmGetContribution(olsmFullDecompList[[x]], modelFeatureList$depVar,x)) geoContrTable <- Reduce(function(x, y) merge(x, y, all=TRUE), geoContrList) # Random Effect by geography randomEffect <- data.frame(Term = rownames(t(random.effects(model))),data.frame(t(random.effects(model)),row.names = NULL)) output <- NULL output <- c(output,"The Mixed Model Result") output <- c(output,"\n\n") output <- c(output,paste("Dependant Variable:",modelFeatureList$depVar)) output <- c(output,"\n\n") output <- c(output,paste("Model Statistics:")) output <- c(output,noquote(capture.output(write.csv(modelFeature,stdout(),row.names = F,quote = F)))) output <- c(output,"\n\n") output <- c(output,paste("Number of Observations Read:",modelFeatureList$TotalDataCount)) output <- c(output,paste("Number of Observations Used:",nrow(model$data))) output <- c(output,"\n\n") output <- c(output,paste("AF Start Date:",modelFeatureList$startDate)) output <- c(output,"\n\n") output <- c(output,paste("AF End Date:",modelFeatureList$endDate)) output <- c(output,"\n\n") output <- c(output,paste("Model Start Date:",min(lubridate::dmy(modelFeatureList$modellingPeriod)))) output <- c(output,"\n\n") output <- c(output,paste("Model End Date:",max(lubridate::dmy(modelFeatureList$modellingPeriod)))) output <- c(output,"\n\n") output <- c(output,paste("Adstock Choice:",modelFeatureList$adStockChoice)) output <- c(output,"\n\n") output <- c(output,paste("Intercept:",modelFeatureList$hasIntercept)) output <- c(output,"\n\n") output <- c(output,paste("Weight:",modelFeatureList$wLSChoice)) output <- c(output,"\n\n") output <- c(output,paste("Intercept:",modelFeatureList$hasIntercept)) output <- c(output,"\n\n") output <- c(output,paste("Mixed Model Chioce:",modelFeatureList$mixedModelChioce)) output <- c(output,"\n\n") output <- c(output,paste("Class Level Information:")) output <- c(output,noquote(capture.output(write.csv(data.frame(Class = "Geography",Levels = length(modelFeatureList$selectedGeos), Values = paste(modelFeatureList$selectedGeos,collapse = "-")),stdout(),row.names = F,quote = F)))) output <- c(output,"\n\n") # output <- c(output,paste("Rolled-up Estimate with Contribution:")) # output <- c(output,noquote(capture.output(write.csv(rolledEstimate,stdout(),row.names = F,quote = F)))) # output <- c(output,"\n\n") # output <- c(output,paste("Unrolled Estimate by Geography:")) # output <- c(output,noquote(capture.output(write.csv(unrolledEstimate,stdout(),row.names = F,quote = F)))) # output <- c(output,"\n\n") # output <- c(output,paste("Unrolled Contribution% by Geography:")) # output <- c(output,noquote(capture.output(write.csv(geoContrTable,stdout(),row.names = F,quote = F)))) # output <- c(output,"\n\n") # output <- c(output,paste("Random Effect for Each Variable within Geography:")) # output <- c(output,noquote(capture.output(write.csv(randomEffect,stdout(),row.names = F,quote = F)))) # output <- c(output,"\n\n") mixedModelOutput <- list() mixedModelOutput[["output"]] <- output mixedModelOutput[["rolledEstimate"]] <- rolledEstimate mixedModelOutput[["unrolledEstimate"]] <- unrolledEstimate mixedModelOutput[["randomEffect"]] <- randomEffect return(mixedModelOutput) #return(output) } olsmExtractModelData <- function(model, olsm_parametersDF, olsmFinalTransRegDf, olsmFinalRegDf,regDf,olsmDummyModelDateScope,modelResult){ olsm_modelData <- NULL olsm_modelData <- model$model # modelDf <- names(model$coefficients) # # # get combined column var # combinedIndex <- grep("Combined",modelDf) # combinedVar <- NULL # if(length(combinedIndex) >= 1){ # combinedColumns <- olsm_parametersDF[which(olsm_parametersDF$Combined_Column != 0),] # combinedColumnsList <- split(combinedColumns$VariableName,combinedColumns$Combined_Column) # combinedColumnsList <- setNames(combinedColumnsList,paste0("Combined_",names(combinedColumnsList))) # combinedVar <- as.character(unlist(lapply(combinedIndex, function(x) combinedColumnsList[[modelDf[x]]]))) # modelDf <- modelDf[-c(combinedIndex)] # removing intercept and combined columns # } # # # get fixed var # fixedVar <- NULL # fixedVar <- olsm_parametersDF[which(olsm_parametersDF$Type %in% c("Fixed Var No Trans","Fixed Var TOF")),c("VariableName")] # # # get depvar # depVar <- as.character(olsm_parametersDF$VariableName[which(olsm_parametersDF$Type == "DepVar")]) # # # remove intercept # if(any(grepl("Intercept", modelDf))){ # modelDf <- modelDf[-grep("Intercept", modelDf)] # } # # # get indepVar # indepVar <- modelDf[!modelDf %in% depVar] # # # if(grepl("Dummy",modelResult[,"Model_No"])){ # dummyScope <- olsmDummyModelDateScope[[modelResult[,"Model_No"]]] # regDf <- data.frame(subset(regDf, dmy(regDf$Period) >= dummyScope$dummyStartDate & dmy(regDf$Period) <= dummyScope$dummyEndDate),row.names = NULL) # olsmFinalTransRegDf <- data.frame(subset(olsmFinalTransRegDf, dmy(olsmFinalTransRegDf$Period) >= dummyScope$dummyStartDate & dmy(olsmFinalTransRegDf$Period) <= dummyScope$dummyEndDate),row.names = NULL) # olsmFinalRegDf <- data.frame(subset(olsmFinalRegDf, dmy(olsmFinalRegDf$Period) >= dummyScope$dummyStartDate & dmy(olsmFinalRegDf$Period) <= dummyScope$dummyEndDate),row.names = NULL) # } # # olsm_modelData <- cbind(olsmFinalTransRegDf[, which(colnames(olsmFinalTransRegDf)== "Period")], # regDf[,which(names(regDf)==depVar)], # olsmFinalRegDf[,which(names(olsmFinalRegDf) %in% indepVar)], # olsmFinalTransRegDf[,which(names(olsmFinalTransRegDf) %in% combinedVar)], # regDf[,which(names(regDf) %in% fixedVar)]) # # colnames(olsm_modelData) <- c("Period", depVar, names(olsmFinalRegDf)[which(names(olsmFinalRegDf) %in% indepVar)],names(olsmFinalTransRegDf)[which(names(olsmFinalTransRegDf) %in% combinedVar)],names(regDf)[which(names(regDf) %in% fixedVar)]) # # if(any(names(olsmFinalRegDf) %in% "Geography")){ # olsm_modelData <- cbind(Geography = olsmFinalRegDf$Geography, olsm_modelData) # } # # if(any(names(olsmFinalRegDf) %in% "weight")){ # olsm_modelData <- cbind(olsm_modelData, Weight = olsmFinalRegDf[,"weight"]) # } # # if(grepl("Dummy",modelResult[,"Model_No"])){ # dummyScope <- olsmDummyModelDateScope[[modelResult[,"Model_No"]]] # olsm_modelData <- data.frame(subset(olsm_modelData, dmy(olsm_modelData$Period) >= dummyScope$dummyStartDate & dmy(olsm_modelData$Period) <= dummyScope$dummyEndDate),row.names = NULL) # df <- cbind(olsm_modelData,model$model[,grep("Dummy",names(model$model))]) # names(df) <- c(names(olsm_modelData),names(model$model)[grep("Dummy",names(model$model))]) # olsm_modelData <- df # } return(olsm_modelData) } olsmExtractBaseFullDecompMinMax <- function(olsmFullDecompList, olsmModelFeatureList){ fullDecompMinMaxAdjustDF <- data.frame(Geography = names(olsmFullDecompList)) for(i in 1:length(olsmModelFeatureList$min_max_var$VariableName)){ var <- olsmModelFeatureList$min_max_var$VariableName[i] if(as.character(olsmModelFeatureList$min_max_var$Min_Max_Adjustment[i]) == "Min"){ df <- data.frame(sapply(olsmFullDecompList, function(x) min(x[,names(x) %in% var], na.rm = T)),row.names = NULL) }else if(as.character(olsmModelFeatureList$min_max_var$Min_Max_Adjustment[i]) == "Max"){ df <- data.frame(sapply(olsmFullDecompList, function(x) max(x[,names(x) %in% var], na.rm = T)),row.names = NULL) }else if(as.character(olsmModelFeatureList$min_max_var$Min_Max_Adjustment[i]) == "Average"){ df <- data.frame(sapply(olsmFullDecompList, function(x) mean(x[,names(x) %in% var], na.rm = T)),row.names = NULL) } names(df) <- var fullDecompMinMaxAdjustDF <- cbind(fullDecompMinMaxAdjustDF, df) } return(fullDecompMinMaxAdjustDF) } olsmMinMaxAdjust <- function(df, modelFeatureList){ min_max_var <- modelFeatureList$min_max_var baseFullDecompMinMix <- modelFeatureList$fullDecompMinMaxAdjustDF df$Geography <- as.character(df$Geography) if(nrow(min_max_var)!= 0){ for(i in 1:nrow(min_max_var)){ if(any(grepl(min_max_var$VariableName[i],names(df)))){ varTmp <- df[,which(grepl(min_max_var$VariableName[i],names(df)))] df[,which(grepl(min_max_var$VariableName[i],names(df)))] <- varTmp-baseFullDecompMinMix[baseFullDecompMinMix$Geography== unique(df$Geography),min_max_var$VariableName[i]] if(modelFeatureList$hasIntercept == "Yes"){ df[,which(grepl("Intercept",names(df)))] <- df[,which(grepl("Intercept",names(df)))] + baseFullDecompMinMix[baseFullDecompMinMix$Geography== unique(df$Geography),min_max_var$VariableName[i]] } } } } return(df) } olsmExtractFullDecompRolledUp <- function(olsmFulldecompUnRolledDf){ olsmFulldecompRolledDf <- olsmFulldecompUnRolledDf olsmFulldecompRolledDf$Geography <- NULL if(!is.Date(olsmFulldecompRolledDf$Period)){ olsmFulldecompRolledDf$Period <- lubridate::dmy(olsmFulldecompRolledDf$Period) } olsmFulldecompRolledDf <- aggregate(olsmFulldecompRolledDf[,-1],by = list(olsmFulldecompRolledDf$Period),sum) names(olsmFulldecompRolledDf)[1] <- "Period" return(olsmFulldecompRolledDf) } olsmExtractFullDecomp <- function(model, olsm_parametersDF, olsmModelData,modelFeatureList, olsm_RegDataTemp, transData){ olsmDenormbyDep <- function(olsmFulldecompDf, depAvg, ModelDf, modelFeatureList, olsm_parametersDF, denormType){ # This function will call if Depvar is Normalized. # And IndepVar may or may not be normalized. # IF IndepVar is Normalized so denormalized indepVar with its estimate and depAvg, # otherwise denormalized indepVar with its estimate only. # DepVar will denormalized by depAvg. # IF Intercept is present then it will get denormalized by depAvg. for (j in 1:length(olsmFulldecompDf)) { #names(olsmFulldecompDf) #j = 9 if(any(ModelDf$term == names(olsmFulldecompDf)[j])){ if(denormType == "Division"){ olsmFulldecompDf[,j] <- olsmFulldecompDf[,j]* ModelDf[which(ModelDf$term == names(olsmFulldecompDf)[j]),2]* depAvg }else if(denormType == "Subtraction"){ olsmFulldecompDf[,j] <- olsmFulldecompDf[,j]* (ModelDf[which(ModelDf$term == names(olsmFulldecompDf)[j]),2]+ depAvg) }else { # indepVar is not normalized. olsmFulldecompDf[,j] <- olsmFulldecompDf[,j]* ModelDf[which(ModelDf$term == names(olsmFulldecompDf)[j]),2] } }else if(names(olsmFulldecompDf)[j] == modelFeatureList$depVar){ if(denormType == "Division"){ olsmFulldecompDf[,j] <- olsmFulldecompDf[,j] * depAvg }else if(denormType == "Subtraction"){ olsmFulldecompDf[,j] <- olsmFulldecompDf[,j] + depAvg } } } if(modelFeatureList$hasIntercept == "Yes"){ if(denormType == "Division"){ intercept <- ModelDf[grep("Intercept",ModelDf$term),2] * depAvg }else if(denormType == "Subtraction"){ intercept <- ModelDf[grep("Intercept",ModelDf$term),2] + depAvg } olsmFulldecompDf <- cbind(olsmFulldecompDf, intercept) names(olsmFulldecompDf)[length(names(olsmFulldecompDf))] <- "Intercept" } return(olsmFulldecompDf) } olsmDenormWithoutDep <- function(olsmFulldecompDf, ModelDf, modelFeatureList, olsm_parametersDF){ # This function will call if Depvar is not Normalized. # And IndepVar may or may not be normalized, and It will get denormalized with its estimate only. # IF Intercept is present then it will just add to data. for (j in 1:length(olsmFulldecompDf)) { if(any(ModelDf$term == names(olsmFulldecompDf)[j])){ olsmFulldecompDf[,j] <- olsmFulldecompDf[,j]* ModelDf[which(ModelDf$term == names(olsmFulldecompDf)[j]),2] } } if(modelFeatureList$hasIntercept == "Yes"){ olsmFulldecompDf <- cbind(olsmFulldecompDf, ModelDf[grep("Intercept",ModelDf$term),2]) names(olsmFulldecompDf)[length(names(olsmFulldecompDf))] <- "Intercept" } return(olsmFulldecompDf) } modelDFList <- list() if(modelFeatureList$mixedModelChioce == "Yes"){ ModelDf <- data.frame(term = rownames(t(coef(model))), t(coef(model)), row.names = NULL) names(ModelDf)[-1] <- modelFeatureList$selectedGeos modelDFList <- lapply(as.list(ModelDf[-1]), function(x){data.frame(ModelDf[1],estimate = x, stringsAsFactors = F)}) }else { ModelDf <- tidy(model) ModelDf <- ModelDf[,names(ModelDf) %in% c("term","estimate")] for(geo in as.character(unique(olsmModelData$Geography))){ modelDFList[[geo]] <- list() modelDFList[[geo]] <- ModelDf } } olsmFullDecomp <- NULL if(any(grepl("Combined",modelDFList[[1]]$term))){ if(modelFeatureList$mixedModelChioce == "Yes"){ geoTransData <- split(transData, transData$Geography) modelDFList <- lapply(names(modelDFList), function(x){return(olsmSplitCombinedEstimateData(modelDFList[[x]], olsm_parametersDF, geoTransData[[x]]))}) names(modelDFList) <- names(geoTransData) }else { #combinedDf <- cbind(rep(paste0("Model_",1), times = nrow(modelDFList[[1]])),rep("No OutsideVar", times = nrow(modelDFList[[1]])),modelDFList[[1]]) combinedDf <- modelDFList[[1]] combinedDf <- olsmSplitCombinedEstimateData(combinedDf, olsm_parametersDF, transData) ModelDf <- data.frame(combinedDf[,which(names(combinedDf) %in% c("term","estimate"))],row.names = NULL) for(geo in as.character(unique(olsmModelData$Geography))){ modelDFList[[geo]] <- list() modelDFList[[geo]] <- ModelDf } } } if(any(grepl("Fixed",olsm_parametersDF$Type))){ if(modelFeatureList$mixedModelChioce == "Yes"){ if(modelFeatureList$FixedVarChoice == "All"){ fixedDf <- data.frame(olsm_parametersDF[grep("Fixed",olsm_parametersDF$Type),names(olsm_parametersDF) %in% c("VariableName","Fixed_Coefficient")],row.names = NULL) names(fixedDf) <- c("term","estimate") modelDFList <- lapply(modelDFList, function(x){return(data.frame(rbind(x,fixedDf), row.names = NULL))}) names(modelDFList) <- as.character(unique(olsmModelData$Geography)) }else if(modelFeatureList$FixedVarChoice == "Geo"){ fixedDfByGeo <- split(modelFeatureList$geoFixedEstimatesDF,modelFeatureList$geoFixedEstimatesDF$Geography) fixedDfByGeo <- lapply(fixedDfByGeo, function(x){data.frame(term = x$VariableName, estimate = x$Fixed_Coefficient, row.names = NULL)}) modelDFList <- lapply(names(modelDFList), function(x){return(data.frame(rbind(modelDFList[[x]],fixedDfByGeo[[x]]), row.names = NULL))}) names(modelDFList) <- as.character(unique(olsmModelData$Geography)) } }else { if(modelFeatureList$FixedVarChoice == "All"){ fixedDf <- data.frame(olsm_parametersDF[grep("Fixed",olsm_parametersDF$Type),names(olsm_parametersDF) %in% c("VariableName","Fixed_Coefficient")],row.names = NULL) names(fixedDf) <- names(modelDFList[[1]]) modelDFList <- lapply(modelDFList, function(x){return(rbind(x,fixedDf))}) }else if(modelFeatureList$FixedVarChoice == "Geo"){ fixedDfByGeo <- split(modelFeatureList$geoFixedEstimatesDF,modelFeatureList$geoFixedEstimatesDF$Geography) fixedDf <- NULL fixedDfByGeo <- lapply(fixedDfByGeo, function(x){ fixedDf <- data.frame(term = x$VariableName, estimate = x$Fixed_Coefficient, row.names = NULL) names(fixedDf) <- names(modelDFList[[1]]) return(fixedDf) }) modelDFList <- lapply(names(modelDFList), function(x){return(rbind(modelDFList[[x]],fixedDfByGeo[[x]]))}) names(modelDFList) <- as.character(unique(olsmModelData$Geography)) } } } indepVar <- as.character(modelDFList[[1]]$term[!modelDFList[[1]]$term %in% modelFeatureList$depVar]) df <- olsm_RegDataTemp[which(dmy(olsm_RegDataTemp$Period) %in% dmy(modelFeatureList$modellingPeriod)),] geoDepMean <- df[df$Geography %in% modelFeatureList$selectedGeos,names(df) %in% c("Geography", modelFeatureList$depVar)] geoDepMean <- aggregate(geoDepMean[,which(names(geoDepMean) %in% modelFeatureList$depVar)], by = list(geoDepMean$Geography), FUN=mean) names(geoDepMean) <- c("Geography", "DepMean") olsmFulldecompDf <- olsmModelData[,names(olsmModelData)%in% c("Geography", "Period", modelFeatureList$depVar,indepVar)] olsmFulldecompList <- split(olsmFulldecompDf, olsmFulldecompDf$Geography) olsmFulldecompList <- lapply(olsmFulldecompList, function(x){if(nrow(x) == 0){return(NULL)}else {return(x)}}) olsmFulldecompList <- olsmFulldecompList[!sapply(olsmFulldecompList,is.null)] if(olsm_parametersDF$Normalization[olsm_parametersDF$Type == "DepVar"] != "None"){ denormType <- as.character(olsm_parametersDF$Normalization[olsm_parametersDF$Type == "DepVar"]) olsmFulldecompList <- lapply(names(olsmFulldecompList), function(x){ #x <- names(olsmFulldecompList)[1] return(olsmDenormbyDep(olsmFulldecompDf = olsmFulldecompList[[x]], depAvg = geoDepMean[geoDepMean$Geography == x,"DepMean"], ModelDf = modelDFList[[x]], modelFeatureList, olsm_parametersDF, denormType)) }) }else if(olsm_parametersDF$Normalization[olsm_parametersDF$Type == "DepVar"] == "None"){ olsmFulldecompList <- lapply(names(olsmFulldecompList), function(x){ return(olsmDenormWithoutDep(olsmFulldecompList[[x]], modelDFList[[x]], modelFeatureList, olsm_parametersDF)) }) } names(olsmFulldecompList) <- unique(olsmFulldecompDf$Geography) return(olsmFulldecompList) } # Function to denorm actual vs Predcited of OLSM Model. olsmDenormActvsPred <- function(modelParam, actPredData, olsm_parametersDF, olsm_RegDataTemp ){ actPredDenormList <- list() actPredDataRolled <- actPredData actPredDataRolled$Geography <- NULL if(class(actPredDataRolled$Period) != "Date"){ actPredDataRolled$Period <- lubridate::dmy(actPredDataRolled$Period) } actPredDataRolled <- aggregate(actPredDataRolled[,-1],by = list(actPredDataRolled$Period),sum) names(actPredDataRolled)[1] <- "Period" actPredDenormList[["actPredDataUnRolled"]] <- actPredData actPredDenormList[["actPredDataRolled"]] <- actPredDataRolled if(olsm_parametersDF$Normalization[which(olsm_parametersDF$Type == "DepVar")] != "None"){ depVar <- olsm_parametersDF$VariableName[olsm_parametersDF$Type == "DepVar"] df <- olsm_RegDataTemp[which(dmy(olsm_RegDataTemp$Period) %in% dmy(modelParam$modellingPeriod)),] geoDepMean <- df[df$Geography %in% modelParam$selectedGeos,names(df) %in% c("Geography", depVar)] geoDepMean <- aggregate(geoDepMean[,which(names(geoDepMean) %in% depVar)], by = list(geoDepMean$Geography), FUN=mean) names(geoDepMean) <- c("Geography", "DepMean") actPredList <- split(actPredData, actPredData$Geography) actPredList <- lapply(actPredList, function(x){if(nrow(x) == 0){return(NULL)}else {return(x)}}) actPredList <- actPredList[!sapply(actPredList,is.null)] for(i in 1:length(actPredList)){ # i = 1 depAvg <- geoDepMean$DepMean[geoDepMean$Geography == names(actPredList)[i]] if(olsm_parametersDF$Normalization[which(olsm_parametersDF$Type == "DepVar")] == "Division"){ actPredList[[i]][,-c(1,2)] <- actPredList[[i]][,-c(1,2)] * depAvg }else if(olsm_parametersDF$Normalization[which(olsm_parametersDF$Type == "DepVar")] == "Subtraction"){ if(modelParam$MixedModelChoice == "No"){ actPredList[[i]][,-c(1,2,5)] <- actPredList[[i]][,-c(1,2,5)] + depAvg }else if(modelParam$MixedModelChoice == "Yes"){ actPredList[[i]][,!names(actPredList[[i]]) %in% c("Geography","Period", names(actPredList[[i]])[grep("Residuals",names(actPredList[[i]]))])] <- actPredList[[i]][,!names(actPredList[[i]]) %in% c("Geography","Period", names(actPredList[[i]])[grep("Residuals",names(actPredList[[i]]))])] + depAvg } } } actPredDataUnRolled <- as.data.frame(rbindlist(actPredList)) actPredDataRolled <- actPredDataUnRolled actPredDataRolled$Geography <- NULL if(class(actPredDataRolled$Period) != "Date"){ actPredDataRolled$Period <- lubridate::dmy(actPredDataRolled$Period) } actPredDataRolled <- aggregate(actPredDataRolled[,-1],by = list(actPredDataRolled$Period),sum) names(actPredDataRolled)[1] <- "Period" actPredDenormList[["actPredDataUnRolled"]] <- actPredDataUnRolled actPredDenormList[["actPredDataRolled"]] <- actPredDataRolled } return(actPredDenormList) } olsmExtractAllModelData <- function(olsmAllModelList, olsm_parametersDF,hasIntercept, olsmFinalTransRegDf){ olsmModelDataList <- lapply(olsmAllModelList, function(x) as.data.frame(tidy(x))) olsmModelDataDfFinal <- NULL parColName <- names(olsm_parametersDF)[which(!names(olsm_parametersDF) %in% c("VariableName","Type" ))] fixedVar <- olsm_parametersDF[which(olsm_parametersDF$Type %in% c("Fixed Var No Trans","Fixed Var TOF")),c("VariableName", "Fixed_Coefficient")] for (i in 1:length(olsmModelDataList)) { #i = 2 ModelDf <- as.data.frame(tidy(olsmAllModelList[[i]])) # getting VIF of variable VIF <- rbind(data.frame(term = "(Intercept)", VIF = NA), data.frame(term = names(rms::vif(olsmAllModelList[[i]])), VIF = rms::vif(olsmAllModelList[[i]]),row.names = NULL)) ModelDf <- merge(ModelDf, VIF, by = "term") outsideVarfull <- ModelDf$term[gsub("_L+[0-9].*","",ModelDf$term) %in% olsm_parametersDF$VariableName[grep("Outside",olsm_parametersDF$Type)]] outsideVar <- gsub("_L+[0-9].*","",outsideVarfull[!is.na(outsideVarfull)]) term <- list() if(length(outsideVar)==0){ olsmModelDataDf <- cbind(rep(paste0("Model_",i), times = nrow(ModelDf)),rep("No OutsideVar", times = nrow(ModelDf)),ModelDf) if(any(grepl("Combined",olsmModelDataDf$term))==TRUE){ names(olsmModelDataDf)[1:2] <- c("Model_Number","Outside_Variable") olsmModelDataDf <- olsmSplitCombinedEstimateData(olsmModelDataDf, olsm_parametersDF, olsmFinalTransRegDf) } colnames(olsmModelDataDf) <- c("Model_Number", "Outside_Variable","Model Terms", "Estimate", "Std.Error", "Statistic","p.Value","VIF") if(nrow(fixedVar)!=0){ fixedVarDf <- data.frame(rep(olsmModelDataDf$Model_Number[1],nrow(fixedVar)), olsmModelDataDf$Outside_Variable[1],fixedVar$VariableName, fixedVar$Fixed_Coefficient, NA, NA, NA, NA) colnames(fixedVarDf) = c("Model_Number","Outside_Variable","Model Terms","Estimate","Std.Error","Statistic","p.Value","VIF") olsmModelDataDf <- rbind(olsmModelDataDf, fixedVarDf) } orderModelTerm <- olsmModelDataDf$`Model Terms` linearDecayVarPos <- grep("_L+[0-9].*", orderModelTerm) orderTerm <- gsub("_L+[0-9].*","",orderModelTerm) if(hasIntercept == "Yes"){ term[[orderModelTerm[1]]] <- olsm_parametersDF[which(olsm_parametersDF$Type == "DepVar"),] term$`(Intercept)`[1] <- "(Intercept)" term <- append(term,sapply(orderTerm[-1], function(x) term[[x]] <- olsm_parametersDF[which(olsm_parametersDF$VariableName == x),],simplify = FALSE)) }else { term <- append(term,sapply(orderTerm, function(x) term[[x]] <- olsm_parametersDF[which(olsm_parametersDF$VariableName == x),],simplify = FALSE)) } parDf <- as.data.frame(rbindlist(term)) parDf$VariableName[linearDecayVarPos] <- olsmModelDataDf$`Model Terms`[linearDecayVarPos] olsmModelDataDf <- merge(olsmModelDataDf, parDf, by.x = "Model Terms", by.y = "VariableName",all.x = TRUE) olsmModelDataDf[which(olsmModelDataDf$Type == "DepVar"),parColName] <- NA olsmModelDataDf[which(olsmModelDataDf$Type %in% c("Fixed Var No Trans")),parColName[-which(parColName=="Fixed_Coefficient")]] <- NA olsmModelDataDf[which(olsmModelDataDf$Type %in% c("Fixed Var TOF")),parColName[-which(parColName %in% c("Transformation","DecayMin","AlphaMin","BetaMin","Normalization","Fixed_Coefficient"))]] <- NA olsmModelDataDf <- olsmModelDataDf[,c("Model_Number","Outside_Variable","Model Terms","Estimate","Std.Error","Statistic","p.Value","VIF",names(parDf)[-1])] olsmModelDataDf <- olsmModelDataDf[match(orderModelTerm, olsmModelDataDf$`Model Terms`),] # if(is.null(olsmModelDataDfFinal)) if(i > 1){ olsmModelDataDfFinal <- rbind(olsmModelDataDfFinal, olsmModelDataDf) }else { olsmModelDataDfFinal <- olsmModelDataDf } }else{ df <- cbind(rep(paste0("Model_",i), times = nrow(ModelDf)),rep(outsideVar, times = nrow(ModelDf)),ModelDf) if(any(grepl("Combined",df$term))==TRUE){ names(olsmModelDataDf)[1:2] <- c("Model_Number","Outside_Variable") df <- olsmSplitCombinedEstimateData(parameterDetails = df,parametersDf = olsm_parametersDF, transData = olsmFinalTransRegDf) } colnames(df) <- c("Model_Number", "Outside_Variable","Model Terms", "Estimate", "Std.Error", "Statistic","p.Value","VIF") df$`Model Terms` <- gsub("_L+[0-9].*","",df$`Model Terms`) if(nrow(fixedVar)!= 0){ fixedVarDf <- data.frame(rep(df$Model_Number[1],nrow(fixedVar)), df$Outside_Variable[1],fixedVar$VariableName, fixedVar$Fixed_Coefficient, NA, NA, NA,NA) colnames(fixedVarDf) = c("Model_Number","Outside_Variable","Model Terms","Estimate","Std.Error","Statistic","p.Value","VIF") df <- rbind(df, fixedVarDf) } orderTerm <- df$`Model Terms` indvar <- orderTerm[!orderTerm %in% outsideVarfull] if(hasIntercept == "Yes"){ term[[orderTerm[1]]] <- olsm_parametersDF[which(olsm_parametersDF$Type == "DepVar"),] term$`(Intercept)`[1] <- "(Intercept)" indvar <- indvar[-1] } term <- append(term,sapply(indvar, function(x) term[[x]] <- olsm_parametersDF[which(olsm_parametersDF$VariableName == x),],simplify = FALSE)) term <- append(term,sapply(outsideVar, function(x) term[[x]] <- olsm_parametersDF[which(olsm_parametersDF$VariableName == x),],simplify = FALSE)) parDf <- as.data.frame(rbindlist(term)) if(any(grepl("Dummy",names(term)))){ dummyDf <- as.data.frame(t(sapply(names(term)[grep("Dummy",names(term))], function(x) c(x,rep(NA,length(parDf)-1))))) names(dummyDf) <- names(parDf) parDf <- rbind(parDf,dummyDf) } df <- merge(df, parDf, by.x = "Model Terms", by.y = "VariableName") df <- df[,c("Model_Number","Outside_Variable","Model Terms","Estimate","Std.Error","Statistic","p.Value","VIF",names(parDf)[-1])] df$`Model Terms`[df$`Model Terms` %in% outsideVar] <- outsideVarfull orderTerm[orderTerm == outsideVar] <- outsideVarfull df <- df[match(orderTerm, df$`Model Terms`),] df[which(df$Type == "DepVar"),parColName] <- NA df[which(df$Type %in% c("Fixed Var No Trans")),parColName[-which(parColName=="Fixed_Coefficient")]] <- NA olsmModelDataDfFinal <- rbind(olsmModelDataDfFinal, df) } } # Separate Columns for Outside variables olsmModelDataDfFinal$S.No <- seq(1:nrow(olsmModelDataDfFinal)) olsm_Outside_Data <- olsmModelDataDfFinal[which(olsmModelDataDfFinal$Type == "Outside TOF"),] termData <- data.frame() for(term in olsm_Outside_Data$`Model Terms`){ # first extracting "L\\d" and than extracting "\\d"= digit and assigning it as Lag . Outside_Lag <- as.numeric(str_extract(stringr::str_extract(term,"L\\d+"),pattern = "\\d.*")) # first extracting "D\\d\\.\\d+" -> D followed by decimal and than extracting "\\d.*"= digit and assigning it as Decay(+ is to take more than one digits). Outside_Decay <- as.numeric(str_extract(stringr::str_extract(term,"D\\d\\.\\d+"),pattern = "\\d.*")) # first extracting "A\\d\\.\\d+" -> A followed by decimal and than extracting "\\d.*"= digit and assigning it as alpha. Outside_Alpha <- as.numeric(str_extract(stringr::str_extract(term,"A\\d\\.\\d+"),pattern = "\\d.*")) if(is.na(Outside_Alpha)){ Outside_Alpha <- as.numeric(str_extract(stringr::str_extract(term,"P\\d\\.\\d+"),pattern = "\\d.*")) } # first extracting "B\\d\\.\\d+" -> B followed by decimal and than extracting "\\d.*"= digit and assigning it as beta. Outside_Beta <- as.numeric(str_extract(stringr::str_extract(term,"B\\d\\.\\d+"),pattern = "\\d.*")) if(is.na(Outside_Beta)){ Outside_Beta <- as.numeric(str_extract(stringr::str_extract(term,"B\\d+"),pattern = "\\d.*")) } if(length(nrow(termData)) == 0){ termData <- data.frame(Outside_Lag=Outside_Lag,Outside_Alpha=Outside_Alpha,Outside_Beta=Outside_Beta,Outside_Decay=Outside_Decay) } else{ # making dataframe with variable ,lag, decay, alpha, beta and rbinding it to termData termData <- rbind(termData,data.frame(Outside_Lag=Outside_Lag,Outside_Alpha=Outside_Alpha,Outside_Beta=Outside_Beta,Outside_Decay=Outside_Decay)) } } if(nrow(olsm_Outside_Data)!=0){ olsm_toMerge_OutsideData <- data.frame(cbind(S.No = olsm_Outside_Data$S.No,termData),stringsAsFactors = T) olsmModelDataDfFinal <- merge(olsmModelDataDfFinal,olsm_toMerge_OutsideData,by = "S.No",all = T) olsmModelDataDfFinal$S.No <- NULL olsmModelDataDfFinal$`Model Terms` <- gsub(pattern = "\\_\\L\\d.*$","",olsmModelDataDfFinal$`Model Terms`) olsmModelDataDfFinal <- olsmModelDataDfFinal[,c("Model_Number","Outside_Variable","Model Terms", "Estimate","Std.Error","Statistic","p.Value","VIF", "Type" ,"Transformation","Decay","Outside_Lag","Outside_Alpha","Outside_Beta","Outside_Decay","LagMin" ,"LagMax","DecaySteps","DecayMin","DecayMax","AlphaSteps","AlphaMin","AlphaMax","BetaMin","BetaMultiplier","BetaSteps","SeriesMax","Normalization","Min_Max_Adjustment","Fixed_Coefficient","Combined_Column","Random_Effect")] } return(olsmModelDataDfFinal) } ##################### Function related MMM Modelling ########################## # function to transform and normalized the data by geography. olsmGetTransformData <- function(olsm_SplitByGeoList,olsm_parametersDF,modelFeatureList){ olsm_splitByGeoSubset <- olsm_SplitByGeoList[which(names(olsm_SplitByGeoList) %in% modelFeatureList$selectedGeos)] splitDfList <- olsm_splitByGeoSubset olsmFinalTransRegList <- NULL splitDf <- NULL for (name in names(splitDfList)) { # name <- names(splitDfList)[1] #print(name) modelFeatureList[["TransGeo"]] <- name dfdata <- createOlsmTransformation(olsm_RegDataModelList = splitDfList, olsm_parametersDF = olsm_parametersDF,modelFeatureList = modelFeatureList) dfdata <- data.frame(dfdata,stringsAsFactors = FALSE) dfdata <- dfdata[dmy(dfdata$Period) %in% dmy(modelFeatureList$modellingPeriod),] olsmFinalTransRegList[[name]] <- dfdata dfdata <- createOlsmNormalization(olsmFinalRegDf = dfdata,olsm_parametersDF) splitDf[[name]]<- dfdata } olsmTransDFList <- NULL olsmTransDFList[["olsmFinalTransRegDf"]] <- as.data.frame(rbindlist(olsmFinalTransRegList)) olsmTransDFList[["olsmFinalNormRegDf"]] <- as.data.frame(rbindlist(splitDf)) return(olsmTransDFList) } # function to call for Modelling olsmGenerateModel <- function(olsm_RegDataTemp,olsm_parametersDF,olsm_SplitByGeoList,modelFeatureList,type){ olsmTransDFList <- olsmGetTransformData(olsm_SplitByGeoList,olsm_parametersDF,modelFeatureList) olsmFinalTransRegDf <- olsmTransDFList$olsmFinalTransRegDf olsmFinalNormRegDf <- olsmTransDFList$olsmFinalNormRegDf olsmFinalRegDf <- olsmGetFixedEffectDF(olsmFinalRegDf = olsmFinalNormRegDf, olsm_parametersDF, modelFeatureList) olsmFinalRegDf <- olsmCreateCombinedColumn(df = olsmFinalRegDf, combinedCol = olsm_parametersDF[olsm_parametersDF$Type != "Not in Model",c("VariableName","Combined_Column")]) modelParamList <- list() if(type == "OLS"){ # OLS stacked modelling formulaList <- olsmBuildFormula(olsmFinalRegDf, olsm_parametersDF,modelFeatureList$hasIntercept, mixed = FALSE) modelParamList <- c("OLS", list(formulaList)) names(modelParamList) <- c("type", "formulaList") olsmAllModelList <- olsmAllPossibleRegressions(modelParamList,olsmFinalRegDf) olsmModelResult <- olsmExtractModelParameter(olsmAllModelList, olsm_parametersDF) }else if(type == "WLS"){ depVar <- olsm_parametersDF$VariableName[olsm_parametersDF$Type == "DepVar"] regDF <- olsm_RegDataTemp[dmy(olsm_RegDataTemp$Period) %in% dmy(modelFeatureList$modellingPeriod),] geoMean <- aggregate(regDF[, which(names(regDF) %in% depVar)], list(regDF$Geography), mean) olsmFinalRegDf <- merge(olsmFinalRegDf, geoMean, by.x = "Geography", by.y = "Group.1") colnames(olsmFinalRegDf)[names(olsmFinalRegDf)%in% "x"] <- "weight" formulaList <- olsmBuildFormula(olsmFinalRegDf, olsm_parametersDF,modelFeatureList$hasIntercept, mixed = FALSE) modelParamList <- c("WLS", list(formulaList)) names(modelParamList) <- c("type", "formulaList") olsmAllModelList <- olsmAllPossibleRegressions(modelParamList,olsmFinalRegDf) olsmModelResult <- olsmExtractModelParameter(olsmAllModelList, olsm_parametersDF) }else if(type == "Mixed"){ # Mixed Modelling formulaList <- olsmBuildFormula(olsmFinalRegDf, olsm_parametersDF,modelFeatureList$hasIntercept, mixed = TRUE) # Formula Building for Mixed Model randomVar <- olsm_parametersDF$VariableName[olsm_parametersDF$VariableName != olsm_parametersDF$VariableName[olsm_parametersDF$Type == "DepVar"] & olsm_parametersDF$Random_Effect == 1 & olsm_parametersDF$Type != "Not in Model"] combinedVar <- unique(olsm_parametersDF$Combined_Column[olsm_parametersDF$Type != "Not in Model" & olsm_parametersDF$Random_Effect == 1 ]) if(any(combinedVar==0)){ combinedVar <- combinedVar[combinedVar!=0] } if(length(combinedVar)!= 0){ randomVar <- c(randomVar,paste0("Combined_",combinedVar)) } olsm_varTypeDf <- olsm_parametersDF[,c("VariableName","Type","Combined_Column")] uniqueCombValue <- plyr::count(as.factor(olsm_varTypeDf$Combined_Column)) combinedColumns <- as.character(olsm_varTypeDf[-c(which(olsm_varTypeDf$Combined_Column==uniqueCombValue[which(uniqueCombValue$freq <= 1),1]),which(olsm_varTypeDf$Combined_Column==uniqueCombValue[which(uniqueCombValue$x == 0),1])),1]) randomVar <- randomVar[!randomVar %in% combinedColumns] randomVar <- paste0(randomVar,collapse = "+") if(modelFeatureList$hasIntercept=="No"){ randomVar <- paste0("0 + ",randomVar) }else if(modelFeatureList$hasIntercept=="Yes"){ randomVar <- paste0("1 + ",randomVar) } modelParamList <- c("Mixed", formulaList, randomVar) names(modelParamList) <- c("type", "formulaList", "randomVar") if(modelFeatureList$wLSChoice == "No"){ modelParamList[["weight"]] <- FALSE olsmAllModelList <- olsmAllPossibleRegressions(modelParamList,olsmFinalRegDf) }else if(modelFeatureList$wLSChoice == "Yes"){ modelParamList[["weight"]] <- TRUE modelParamList[["depVar"]] <- olsm_parametersDF$VariableName[olsm_parametersDF$Type == "DepVar"] geoMean <- aggregate(olsmFinalTransRegDf[, which(names(olsmFinalTransRegDf) %in% modelParamList$depVar)], list(olsmFinalTransRegDf$Geography), mean) olsmFinalRegDf <- merge(olsmFinalRegDf, geoMean, by.x = "Geography", by.y = "Group.1") colnames(olsmFinalRegDf)[names(olsmFinalRegDf)%in% "x"] <- "Geoweight" olsmAllModelList <- olsmAllPossibleRegressions(modelParamList,olsmFinalRegDf) } } olsmResult <- NULL olsmResult[["olsmFinalTransRegDf"]] <- olsmFinalTransRegDf olsmResult[["olsmFinalNormRegDf"]] <- olsmFinalNormRegDf olsmResult[["olsmFinalRegDf"]] <- olsmFinalRegDf olsmResult[["olsmAllModelList"]] <- olsmAllModelList olsmResult[["formulaList"]] <- formulaList if(type != "Mixed"){ olsmResult[["olsmModelResult"]] <- olsmModelResult } return(olsmResult) } # function to call for Modelling olsmBuildFormula <- function(olsmFinalRegDf, olsm_parametersDF, hasIntercept, mixed){ varName <- names(olsmFinalRegDf) olsm_varTypeDf <- olsm_parametersDF[,c("VariableName","Type","Combined_Column")] uniqueCombValue <- plyr::count(as.factor(olsm_varTypeDf$Combined_Column)) combinedColumns <- olsm_varTypeDf[-c(which(olsm_varTypeDf$Combined_Column==uniqueCombValue[which(uniqueCombValue$freq <= 1),1]),which(olsm_varTypeDf$Combined_Column==uniqueCombValue[which(uniqueCombValue$x == 0),1])),] formulaList <- list() depVar <- as.character(olsm_parametersDF$VariableName[which(olsm_parametersDF$Type == "DepVar")]) baseFormula <- paste0(depVar," ~ ") # generating formula without intercept. if(hasIntercept=="No"){ baseFormula <- paste0(depVar," ~ ","0 +") } IndepVariable <- olsm_varTypeDf$VariableName[which(olsm_varTypeDf$Type %in% c("Manual No Trans","Manual TOF"))] if(nrow(combinedColumns)!=0){ IndepVariable <- IndepVariable[-which(IndepVariable %in% combinedColumns$VariableName)] IndepVariable <- c(IndepVariable, names(combinedColumnsList)) } firstFormula <- paste0(paste0(baseFormula,paste(IndepVariable[-length(IndepVariable)],"+",collapse = " " ),collapse = " ")," ",IndepVariable[length(IndepVariable)]) formulaList[[1]]<- firstFormula baseFormula <- paste0(baseFormula,paste(IndepVariable,"+",collapse = " " ),collapse = " ") linearDecayList <- NULL outsideLinear <- olsm_varTypeDf$VariableName[which(olsm_varTypeDf$Type == "Outside No Trans")] outsideTOF <- olsm_varTypeDf$VariableName[which(olsm_varTypeDf$Type == "Outside TOF")] if(any(grepl("Outside",olsm_parametersDF$Type))){ if(length(outsideLinear)>0){ for (i in 1:length(outsideLinear)) { formulaCount <- length(formulaList) formulaList[[formulaCount+1]] <- paste0(baseFormula," ",outsideLinear[i]) } } if(length(outsideTOF)>0){ for (i in 1:length(outsideTOF)) { outsideTOFVar <- outsideTOF[i] varTOF <- varName[gsub("_L+[0-9].*","",varName) == outsideTOFVar] for (j in 1:length(varTOF)) { formulaCount <- length(formulaList) formulaList[[formulaCount+1]] <- paste0(baseFormula," ",varTOF[j]) } } } } return(formulaList) } # function for modelling the data olsmAllPossibleRegressions <- function(modelParamList,olsmFinalRegDf){ if(modelParamList$type == "OLS"){ olsmFinalRegDf <- as.data.frame(lapply(olsmFinalRegDf, function(x) as.numeric(as.character(x)))) olsmModelsResults <- lapply(modelParamList$formulaList,function(x, data) lm(x, data=olsmFinalRegDf,na.action = na.exclude),data=olsmFinalRegDf) }else if(modelParamList$type == "WLS"){ if(any(names(olsmFinalRegDf) %in% c("Geography","Period"))){ modelScopeDfFinal <- olsmFinalRegDf[,-which(names(olsmFinalRegDf) %in% c("Geography","Period"))] }else{ modelScopeDfFinal <- olsmFinalRegDf } modelScopeDfFinal <- as.data.frame(lapply(modelScopeDfFinal, function(x) as.numeric(as.character(x)))) olsmModelsResults <- lapply(modelParamList$formulaList,function(x, data) lm(x, data=modelScopeDfFinal, weights = weight,na.action = na.exclude),data=modelScopeDfFinal) }else if(modelParamList$type == "Mixed"){ # function to generate Mixed model and remove sign flipage iteratively. randModelFunction <- function(fixedFormula,randFormula,nlmeData, modelParamList){ if(modelParamList$weight == FALSE){ mixedeffectmodel <- lme(fixed = as.formula(fixedFormula), random = list(Geography = pdDiag(randFormula)),data = nlmeData, method = "REML", correlation = NULL,weights = NULL,contrasts = NULL,control = lmeControl(maxIter = 500000,msMaxIter = 500000,tolerance = 1e-6,niterEM = 25,msMaxEval = 200,msTol = 1e-12,msVerbose = F,returnObject = TRUE,gradHess = TRUE,apVar = TRUE,minAbsParApVar = 0.05,opt = "nlminb",optimMethod = "BFGS")) }else if(modelParamList$weight == TRUE){ #varFixed(value = eval(parse(text = paste0("~ 1/Geoweight")))) mixedeffectmodel <- lme(fixed = as.formula(fixedFormula),random = list(Geography = pdDiag(randFormula)),data = nlmeData, method = "REML", correlation = NULL,weights = varFixed(value = ~ 1/Geoweight),contrasts = NULL, control = lmeControl(maxIter = 500000,msMaxIter = 500000,tolerance = 1e-6,niterEM = 25,msMaxEval = 200,msTol = 1e-12,msVerbose = F,returnObject = TRUE,gradHess = TRUE,apVar = TRUE,minAbsParApVar = 0.05,opt = "nlminb",optimMethod = "BFGS")) } estimateDf <- as.data.frame.list(coef(mixedeffectmodel)) fixedDf <- fixef(mixedeffectmodel) if(any(grepl("Intercept", names(estimateDf)))){ names(estimateDf)[1] <- "Intercept" names(fixedDf)[1] <- "Intercept" } estimateDfRatio <- NULL for(name in names(fixedDf)){ estimateDfRatio <- estimateDf[,name]* fixedDf[name] estimateDf[,name] <- estimateDfRatio } getcolnameList <- as.list(NULL) getcolnameList <- apply(estimateDf,2,function(x){ if(!any(x < 0)){ return(NULL) }else{ as.vector(which(x < 0)) } }) if(any(grepl("Intercept",names(getcolnameList)))){ getcolnameList[[grep("Intercept",names(getcolnameList))]] <- NULL } if(any(unlist(lapply(getcolnameList,FUN = function(x) length(x))))){ print("Flipped") splitData_geography <- split(nlmeData,nlmeData[,"Geography"]) for(name in names(getcolnameList)){ if(length(getcolnameList[[name]]) != 0){ for(i in 1:length(getcolnameList[[name]])){ splitData_geography[[getcolnameList[[name]][i]]][,name] <- 0 } } } nlmeData <- as.data.frame(rbindlist(splitData_geography,fill = T)) randModelFunction(fixedFormula,randFormula,nlmeData, modelParamList) }else{ return(mixedeffectmodel) } } modelScopeDfFinal <- olsmFinalRegDf[,-which(names(olsmFinalRegDf) %in% c("Period"))] modelScopeDfFinal[,-which(names(modelScopeDfFinal) %in% c("Geography"))] <- as.data.frame(lapply(modelScopeDfFinal[,-which(names(modelScopeDfFinal) %in% c("Geography"))], function(x) as.numeric(as.character(x)))) # getting random part of lme formula. randFormula <- eval(parse(text = as.character(paste0("~ ",modelParamList$randomVar)))) # checking sign flipage in Mixed Model. olsmModelsResults <- lapply(modelParamList$formulaList, function(x,randFormula, data, modelParamList) randModelFunction(fixedFormula = x,randFormula = randFormula,nlmeData = data, modelParamList = modelParamList), randFormula = randFormula, data = modelScopeDfFinal, modelParamList = modelParamList) } return(olsmModelsResults) } # Generate Dummy Model olsmGetDummyModelResult <- function(olsmAllModelList, olsmModelResult,olsmModelScopeDummyTable, finalDf, olsm_parametersDF,dummyModelProp){ if(grepl("Dummy",olsmModelResult$Model_No[as.numeric(dummyModelProp$olsm.model.index)])){ baseModel <- as.numeric(gsub("Model_|_Dummy_+[0-9]*","",olsmModelResult$Model_No[as.numeric(dummyModelProp$olsm.model.index)])) model <- olsmAllModelList[[baseModel]] }else { model <- olsmAllModelList[[as.numeric(dummyModelProp$olsm.model.index)]] } # Dummy model Data dummyModelData <- data.frame(finalDf$Geography,lubridate::dmy(finalDf$Period),model$model) if(any(grepl("weights",names(dummyModelData)))){ names(dummyModelData) <- c("Geography","Period",names(model$model)[-length(model$model)],"weight") }else{ names(dummyModelData) <- c("Geography","Period",names(model$model)) } dummyModelData <- subset(dummyModelData, Period >= min(olsmModelScopeDummyTable$Period) & Period <= max(olsmModelScopeDummyTable$Period)) dummyDFTable <- as.data.frame(olsmModelScopeDummyTable[,which(names(olsmModelScopeDummyTable) %in% names(which(apply(olsmModelScopeDummyTable[,!names(olsmModelScopeDummyTable) %in% c("Geography","Period")],2,sum)!=0)))]) names(dummyDFTable) <- names(which(apply(olsmModelScopeDummyTable[,!names(olsmModelScopeDummyTable) %in% c("Geography","Period")],2,sum)!=0)) dummyModelData <- cbind(dummyModelData,dummyDFTable) dummyModelData <- dummyModelData[,!names(dummyModelData) %in% c("Geography","Period")] depVar <- olsm_parametersDF$VariableName[olsm_parametersDF$Type == "DepVar"] if(dummyModelProp$WLSChoice == "No"){ indepVar <- names(dummyModelData)[!names(dummyModelData) %in% depVar] }else if(dummyModelProp$WLSChoice == "Yes"){ indepVar <- names(dummyModelData)[!names(dummyModelData) %in% c(depVar,"weight")] } if(dummyModelProp$hasIntercept == "No"){ baseFormula <- paste0(depVar," ~ ", paste0(c(indepVar,0),collapse = "+")) }else if(dummyModelProp$hasIntercept == "Yes"){ baseFormula <- paste0(depVar," ~ ", paste0(indepVar,collapse = "+")) } dummyFormula <- list() dummyFormula[[1]] <- baseFormula modelParamList <- list() if(dummyModelProp$WLSChoice == "No"){ # Stacked OLS Model modelParamList <- c("OLS", as.list(dummyFormula)) names(modelParamList) <- c("type", "formulaList") modelDummy <- olsmAllPossibleRegressions(modelParamList,olsmFinalRegDf = dummyModelData) }else if(dummyModelProp$WLSChoice == "Yes"){ # Stacked WLS Model modelParamList <- c("WLS", dummyFormula) names(modelParamList) <- c("type", "formulaList") modelDummy <- olsmAllPossibleRegressions(modelParamList,dummyModelData) } return(modelDummy) } #Creating Respose curve Data CreateResponseCurve <- function(ResponseVariables,ResponseSteps,ResponseMaxLimit,MEUserProjectList){ ResponseCurveDataList <- list() #Response curve series ResponseCurveSeries <- data.frame(ResponseSeries = seq(0,ResponseMaxLimit,ResponseSteps)) #Parameter taking from Global variable Parameters <- data.frame(MEUserProjectList$olsm_parametersDF[which(MEUserProjectList$olsm_parametersDF$VariableName %in% ResponseVariables),c("VariableName","Transformation","BetaMin","AlphaMin","SeriesMax")],row.names = NULL) ResponseTransformation <- data.frame(ResponseCurveSeries) if(MEUserProjectList$olsmModelFeatureList$ScurveSeriesMaxChoice == "Geo"){ seriesMaxGeoDf <- MEUserProjectList$olsmModelFeatureList$ScurveSeriesMaxDF if(any(seriesMaxGeoDf$VariableName == ResponseVariables)){ Parameters$SeriesMax <- mean(seriesMaxGeoDf$SeriesMax[seriesMaxGeoDf$VariableName == ResponseVariables],na.rm = T) } } for(name in 1:nrow(Parameters)){ # name <- 1 if(Parameters[name,"Transformation"] == "S-Curve"){ tempData <- data.frame(TransformedData = (Parameters[,"BetaMin"][name]/(10^10))^(Parameters[,"AlphaMin"][name]^((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100)),stringsAsFactors = F) }else if(Parameters[name,"Transformation"] == "S-origin"){ tempData <- data.frame(TransformedData = (Parameters[,"BetaMin"][name]/(10^9))^(Parameters[,"AlphaMin"][name]^((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100)) - (Parameters[,"BetaMin"][name]/(10^9)),stringsAsFactors = F) }else if(Parameters[name,"Transformation"] == "Power"){ tempData <- data.frame(TransformedData = ResponseCurveSeries^Parameters[,"AlphaMin"][name],stringsAsFactors = F ) } ## BreakThrough & Saturation Points. FirstDerivatives <- data.frame(Parameters[,"AlphaMin"][name]^((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100)*tempData*log(Parameters[,"AlphaMin"][name])*log(Parameters[,"BetaMin"][name]/(10^10))) # 2nd Derivatives SecondDerivatives <- FirstDerivatives*log(Parameters[,"AlphaMin"][name]) + Parameters[,"AlphaMin"][name]^(2*((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100))*tempData*(log(Parameters[,"AlphaMin"][name])^2)*(log((Parameters[,"BetaMin"][name]/(10^10)))^2) # 3rd Derivatives ThirdDerivatives <- FirstDerivatives*log(Parameters[,"AlphaMin"][name])^2+3*Parameters[,"AlphaMin"][name]^(2*((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100))*tempData*log(Parameters[,"AlphaMin"][name])^3*log((Parameters[,"BetaMin"][name]/(10^10)))^2+Parameters[,"AlphaMin"][name]^(3*((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100))*tempData*log(Parameters[,"AlphaMin"][name])^3*log((Parameters[,"BetaMin"][name]/(10^10)))^3 BreakThroughPoint <- ((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100)[match(max(SecondDerivatives[,1],na.rm = T),SecondDerivatives[,1]),] SaturationPoint <- ((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100)[match(max(ThirdDerivatives[match(max(SecondDerivatives[,1],na.rm = T),SecondDerivatives[,1]):nrow(ThirdDerivatives),1],na.rm = T),ThirdDerivatives[,1]),] FullSaturationPoint <- ((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100)[max(which(tempData$ResponseSeries <= 0.98)),] # BP :- BreakThroughPOint, # SP :- SaturationPOint # FSP :- Full Saturation Point BP_XValue <- BreakThroughPoint*((as.numeric(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T))*Parameters[,"SeriesMax"][name])/100) SP_XValue <- SaturationPoint*((max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name])/100) FSP_XValue <- FullSaturationPoint*((max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name])/100) ## X-Axis for BreakThrough & Saturation Point Calculation . XAxis <- ResponseCurveSeries # XAveragePercent <- ((ResponseCurveSeries/(maxSeries*Parameters[,"SeriesMax"][name]))*100) XAveragePercent <- ((ResponseCurveSeries/(max(MEUserProjectList$meRawData[Parameters[name,1]],na.rm = T)*Parameters[,"SeriesMax"][name]))*100) ResponseTransformation <- cbind(ResponseTransformation,XAveragePercent,tempData) names(ResponseTransformation)[length(names(ResponseTransformation))] <- Parameters[name,"VariableName"] names(ResponseTransformation)[c(1,2)] <- c("ResponseCurveSeries","XAveragePercent") } ResponseCurvePlotData <- melt(ResponseTransformation[,!names(ResponseTransformation) %in% "ResponseCurveSeries"],id.vars = c("XAveragePercent")) ### BreakThrough Break_SaturationPlotData <- melt(ResponseTransformation[,!names(ResponseTransformation) %in% "XAveragePercent"],id.vars = c("ResponseCurveSeries")) ResponseCurveDataList[["BP_XValue"]] <- BP_XValue ResponseCurveDataList[["SP_XValue"]] <- SP_XValue ResponseCurveDataList[["FSP_XValue"]] <- FSP_XValue ResponseCurveDataList[["XAxis"]] <- XAxis ResponseCurveDataList[["ResponseCurvePlotData"]] <- ResponseCurvePlotData ResponseCurveDataList[["Break_SaturationPlotData"]] <- Break_SaturationPlotData #returns list of dataframe/s and plot/s return(ResponseCurveDataList) } ################################################################################ ##################### ML_Workbench functions ########################### ################################################################################ # BMA Modelling ML_GetBMAModel <- function(ModelData){ set.seed(100) data <- ModelData data<-Filter(function(x) sd(x) != 0, data) errorflag<-F BMAModel <- NA while(errorflag == F){ tryCatch({ BMAModel <- BMS::bms(data, mprior = "random", g="UIP", user.int=F) errorflag<-T },error=function(e){ },finally={}) } return(BMAModel) } # ML OLS Modelling ML_GetOLSModel <- function(ML_ModelParameterList,varSelectionGrid,ML_ModelDF,norm){ olsParameterList <- list() olsParameterList[["formulaList"]][[1]] <- as.formula(paste0(ML_ModelParameterList$Dependent,"~ ",paste0(varSelectionGrid$Variable[varSelectionGrid$InModel == TRUE],collapse = "+ "))) olsParameterList[["type"]] <- "OLS" modelVar <- c(ML_ModelParameterList[["Dependent"]], as.character(varSelectionGrid[varSelectionGrid$InModel == TRUE,1])) olsModelData <- ML_ModelDF[,modelVar] if(norm == TRUE){ olsModelData<-as.data.frame(lapply(olsModelData, function(x) round((x)/(mean(x)), 5))) } trainPt <- floor(nrow(olsModelData)*(as.numeric(ML_ModelParameterList[["ML_TrainPt"]])/100)) testPt <- floor(nrow(olsModelData)*(as.numeric(ML_ModelParameterList[["ML_TestPt"]])/100)) training<-olsModelData[1:trainPt,] testing<-olsModelData[(nrow(olsModelData)-testPt+1):nrow(olsModelData),] OLSModelList <- list() model <- olsmAllPossibleRegressions(modelParamList = olsParameterList,training) Elasticity <- unlist(lapply(names(model[[1]]$coefficients)[-1], function(x){ y <- pdp::partial(model[[1]],pred.var=x,plot=F,returngrid =TRUE, rug=TRUE,train = training,type="regression") midPoint <- floor(nrow(y)/2) elasticity <- (y[,2]/y[midPoint,2]-1)/(y[,1]/y[midPoint,1]-1) elasticity[is.nan(elasticity)] <- 0 finalAvgEl <- mean(elasticity) })) OLSModelList[["ExternalModel"]] <- model[[1]] OLSModelList[["training"]] <- training OLSModelList[["testing"]] <- testing OLSModelList[["Elasticity"]] <- data.frame(Variable = names(model[[1]]$coefficients)[-1], OLS_Elasticity = Elasticity,row.names = NULL,stringsAsFactors = F) return(OLSModelList) } # Bayesian Modelling using Rstan ML_GetBayesianModel <- function(ML_ModelParameterList,varSelectionGrid,ML_ModelDF,norm){ set.seed(101) modelVar <- c(ML_ModelParameterList[["Dependent"]], as.character(varSelectionGrid[varSelectionGrid$InModel == TRUE,1])) bayesModelData <- ML_ModelDF[,modelVar] names(bayesModelData)[1]<- "Dependant" if(norm == TRUE){ bayesModelData<-as.data.frame(lapply(bayesModelData, function(x) round((x)/(mean(x)), 5))) } ModelConstraintsDF <- varSelectionGrid[,c("Variable","Expected_PostMeanSign")] PosIndex <- which(ModelConstraintsDF$Variable %in% modelVar & ModelConstraintsDF[,2]== 1) NegIndex <- which(ModelConstraintsDF$Variable %in% modelVar & ModelConstraintsDF[,2]== -1) monotonic_constraints <- as.numeric(rep(1,times = 1+length(PosIndex)+length(NegIndex))) monotonic_constraints[NegIndex] <- -1 monotonic_constraints <- monotonic_constraints[-1] trainPt <- floor(nrow(bayesModelData)*(as.numeric(ML_ModelParameterList[["ML_TrainPt"]])/100)) testPt <- floor(nrow(bayesModelData)*(as.numeric(ML_ModelParameterList[["ML_TestPt"]])/100)) training<-bayesModelData[1:trainPt,] testing<-bayesModelData[(nrow(bayesModelData)-testPt+1):nrow(bayesModelData),] bayesModel <-brm(Dependant ~ ., data = training, iter = as.numeric(ML_ModelParameterList[["BayesianParameter"]][["ML_BayesIterNO"]]), chains = as.numeric(ML_ModelParameterList[["BayesianParameter"]][["ML_BayesChains"]]), control = list(adapt_delta = as.numeric(ML_ModelParameterList[["BayesianParameter"]][["ML_BayesAdaptDelta"]]), max_treedepth = as.numeric(ML_ModelParameterList[["BayesianParameter"]][["ML_BayesMaxtreedepth"]]))) Elasticity<- data.frame(Bayesian_Elasticity = fixef(bayesModel)[-1,1]) BayesModelList <- list() BayesModelList[["training"]] <- training BayesModelList[["testing"]] <- testing BayesModelList[["bayes_model"]] <- bayesModel BayesModelList[["Elasticity"]] <- data.frame(Variable = rownames(Elasticity), Bayesian_Elasticity = Elasticity[1],row.names = NULL,stringsAsFactors = F) return(BayesModelList) } # Bayesian Belief Network Modelling ML_GetBayesianBeliefNetworkModel <- function(ML_ModelParameterList,varSelectionGrid,ML_ModelDF,norm){ } # GBM Modelling ML_GetGBMModel <- function(ML_ModelParameterList,varSelectionGrid,ML_ModelDF,norm){ set.seed(101) modelVar <- c(ML_ModelParameterList[["Dependent"]], as.character(varSelectionGrid[varSelectionGrid$InModel == TRUE,1])) gbmModelData <- ML_ModelDF[,modelVar] names(gbmModelData)[1]<- "Dependant" if(norm == TRUE){ gbmModelData<-as.data.frame(lapply(gbmModelData, function(x) round((x)/(mean(x)), 5))) } ModelConstraintsDF <- varSelectionGrid[,c("Variable","Expected_PostMeanSign")] PosIndex <- which(ModelConstraintsDF$Variable %in% modelVar & ModelConstraintsDF[,2]== 1) NegIndex <- which(ModelConstraintsDF$Variable %in% modelVar & ModelConstraintsDF[,2]== -1) monotonic_constraints <- as.numeric(rep(1,times = 1+length(PosIndex)+length(NegIndex))) monotonic_constraints[NegIndex] <- -1 monotonic_constraints <- monotonic_constraints[-1] trainPt <- floor(nrow(gbmModelData)*(as.numeric(ML_ModelParameterList[["ML_TrainPt"]])/100)) testPt <- floor(nrow(gbmModelData)*(as.numeric(ML_ModelParameterList[["ML_TestPt"]])/100)) training<-gbmModelData[1:trainPt,] testing<-gbmModelData[(nrow(gbmModelData)-testPt+1):nrow(gbmModelData),] best_model <- gbm(Dependant ~ .,data = training, var.monotone = monotonic_constraints, bag.fraction = as.numeric(ML_ModelParameterList[["GBMParameter"]][["ML_GBMBagFraction"]]), shrinkage = as.numeric(ML_ModelParameterList[["GBMParameter"]][["ML_GBMShrinkage"]]), n.minobsinnode = as.numeric(ML_ModelParameterList[["GBMParameter"]][["ML_GBMNMinobsinnode"]]), n.trees = as.numeric(ML_ModelParameterList[["GBMParameter"]][["ML_GBMNTree"]]), cv.folds = as.numeric(ML_ModelParameterList[["GBMParameter"]][["ML_GBMCVFolds"]]), distribution = as.character(ML_ModelParameterList[["GBMParameter"]][["ML_GBMDistType"]]), keep.data = FALSE,verbose = F) best.iter <- gbm.perf(best_model,method="cv",plot.it = F) Elasticity <- unlist(lapply(best_model$var.names, function(x){ y <- plot.gbm(best_model,x,n.trees = best.iter,return.grid = TRUE) midPoint <- floor(nrow(y)/2) elasticity <- (y[,2]/y[midPoint,2]-1)/(y[,1]/y[midPoint,1]-1) elasticity[is.nan(elasticity)] <- 0 finalAvgEl <- mean(elasticity) })) GBMModelList <- list() GBMModelList[["GBMModel"]] <- best_model GBMModelList[["GBMBestIteration"]] <- best.iter GBMModelList[["training"]] <- training GBMModelList[["testing"]] <- testing GBMModelList[["Elasticity"]] <- data.frame(Variable = best_model$var.names, GBM_Elasticity = Elasticity,row.names = NULL,stringsAsFactors = F) return(GBMModelList) } # XGBoost Modelling ML_GetXGBoostModel <- function(ML_ModelParameterList,varSelectionGrid,ML_ModelDF,norm){ set.seed(101) modelVar <- c(ML_ModelParameterList[["Dependent"]], as.character(varSelectionGrid[varSelectionGrid$InModel == TRUE,1])) xgboostModelData <- ML_ModelDF[,modelVar] names(xgboostModelData)[1]<- "Dependant" if(norm == TRUE){ xgboostModelData<-as.data.frame(lapply(xgboostModelData, function(x) round((x)/(mean(x)), 5))) } ModelConstraintsDF <- varSelectionGrid[,c("Variable","Expected_PostMeanSign")] PosIndex <- which(ModelConstraintsDF$Variable %in% modelVar & ModelConstraintsDF[,2]== 1) NegIndex <- which(ModelConstraintsDF$Variable %in% modelVar & ModelConstraintsDF[,2]== -1) monotonic_constraints <- as.numeric(rep(1,times = 1+length(PosIndex)+length(NegIndex))) monotonic_constraints[NegIndex] <- -1 monotonic_constraints <- monotonic_constraints[-1] trainPt <- floor(nrow(xgboostModelData)*(as.numeric(ML_ModelParameterList[["ML_TrainPt"]])/100)) testPt <- floor(nrow(xgboostModelData)*(as.numeric(ML_ModelParameterList[["ML_TestPt"]])/100)) train<-xgboostModelData[1:trainPt,] test<-xgboostModelData[(nrow(xgboostModelData)-testPt+1):nrow(xgboostModelData),] trainm <- sparse.model.matrix(Dependant ~ ., data = train) train_label <- train[,"Dependant"] train_matrix <- xgb.DMatrix(data = trainm, label = train_label) testm <- sparse.model.matrix(Dependant~ ., data = test) test_label <- test[,"Dependant"] test_matrix <- xgb.DMatrix(data = testm, label = test_label) watchlist <- list(train = train_matrix, test = test_matrix) crossvalidresult <- xgb.cv(params = list("booster"="gbtree","objective" = "reg:linear","eval_metric" = "rmse"), monotone_constraints = monotonic_constraints, data = train_matrix, early_stopping_rounds = as.numeric(ML_ModelParameterList[["XGBoostParameter"]][["ML_XGBoostESRounds"]]), watchlist = watchlist, eta = as.numeric(ML_ModelParameterList[["XGBoostParameter"]][["ML_XGBoostETA"]]), max.depth = as.numeric(ML_ModelParameterList[["XGBoostParameter"]][["ML_XGBoostMaxDepth"]]), nrounds = as.numeric(ML_ModelParameterList[["XGBoostParameter"]][["ML_XGBoostNrounds"]]), maximize = FALSE, nfold=as.numeric(ML_ModelParameterList[["XGBoostParameter"]][["ML_XGBoostNFolds"]]), verbose = F) best_model <- xgb.train(params = list("booster"="gbtree","objective" = "reg:linear","eval_metric" = "rmse"), monotone_constraints = monotonic_constraints, data = train_matrix, watchlist = watchlist, verbose = F, eta = as.numeric(ML_ModelParameterList[["XGBoostParameter"]][["ML_XGBoostETA"]]), max.depth = as.numeric(ML_ModelParameterList[["XGBoostParameter"]][["ML_XGBoostMaxDepth"]]), nrounds = crossvalidresult$best_iteration, maximize = FALSE) Elasticity <- unlist(lapply(best_model$feature_names, function(x){ y <- partial(best_model,pred.var=x,plot=F,returngrid =TRUE, rug=TRUE,train = sparse.model.matrix(Dependant ~ ., data = train),type="regression") midPoint <- floor(nrow(y)/2) elasticity <- (y$yhat/y$yhat[midPoint]-1)/(y[,1]/y[midPoint,1]-1) elasticity[is.nan(elasticity)] <- 0 finalAvgEl <- mean(elasticity) })) xgboostModelList <- list() xgboostModelList[["best_model"]] <- best_model xgboostModelList[["best.iter"]] <- crossvalidresult xgboostModelList[["training"]] <- train xgboostModelList[["testing"]] <- test xgboostModelList[["Elasticity"]] <- data.frame(Variable = best_model$feature_names, XGBoost_Elasticity = Elasticity,row.names = NULL,stringsAsFactors = F) return(xgboostModelList) } # ANN Modelling ML_GetANNModel <- function(ML_ModelParameterList,varSelectionGrid,ML_ModelDF,norm){ h2o.init() h2o.removeAll() response <- ML_ModelParameterList[["Dependent"]] predictors <- as.character(varSelectionGrid[varSelectionGrid$InModel == TRUE,1]) h2oRawData <- ML_ModelDF[,which(names(ML_ModelDF) %in% c(ML_ModelParameterList[["Dependent"]], as.character(varSelectionGrid[varSelectionGrid$InModel == TRUE,1])))] if(norm == TRUE){ h2oRawData<-as.data.frame(lapply(h2oRawData, function(x) round((x)/(mean(x)), 5))) } trainPt <- floor(nrow(h2oRawData)*(as.numeric(ML_ModelParameterList[["ML_TrainPt"]])/100)) testPt <- floor(nrow(h2oRawData)*(as.numeric(ML_ModelParameterList[["ML_TestPt"]])/100)) Training.hex <- as.h2o(h2oRawData[1:trainPt,]) Testing.hex <- as.h2o(h2oRawData[(nrow(h2oRawData)-testPt+1):nrow(h2oRawData),]) ####hyper_params ### hyper_params <- list(activation=c("Rectifier","Tanh","Maxout","RectifierWithDropout","TanhWithDropout","MaxoutWithDropout"), hidden=list(c(20,20),c(50,50),c(30,30,30),c(25,25,25,25)), input_dropout_ratio=c(0,0.05),l1=seq(0,1e-4,1e-6),l2=seq(0,1e-4,1e-6) ) ####search criteria ### search_criteria = list(strategy = "RandomDiscrete", max_runtime_secs = 360, max_models = as.numeric(ML_ModelParameterList[["ANNParameter"]][["ML_ANNMaxModels"]]), seed=1234567, stopping_rounds=5, stopping_tolerance=1e-2) dl_random_grid <- h2o.grid( algorithm="deeplearning", grid_id = "dl_grid_random", training_frame=Training.hex, validation_frame=Testing.hex, x=predictors, y=response, epochs= as.numeric(ML_ModelParameterList[["ANNParameter"]][["ML_ANNNoEpochs"]]), stopping_metric= as.character(ML_ModelParameterList[["ANNParameter"]][["ML_ANNstoppingmetric"]]), stopping_tolerance=1e-2, ## stop when logloss does not improve by >=1% for 2 scoring events stopping_rounds=2, score_validation_samples=10000, ## downsample validation set for faster scoring score_duty_cycle=0.025, ## don't score more than 2.5% of the wall time max_w2=10, ## can help improve stability for Rectifier hyper_params = hyper_params, search_criteria = search_criteria ) grid <- h2o.getGrid("dl_grid_random",sort_by="r2",decreasing=TRUE) h2obest_model <- h2o.getModel(grid@model_ids[[1]]) Elasticity <- unlist(lapply(h2obest_model@parameters$x, function(x){ y <- h2o.partialPlot(h2obest_model,Training.hex,x,plot = F) midPoint <- floor(nrow(y[1])/2) elasticity <- (y$mean_response/y$mean_response[midPoint]-1)/(y[,1]/y[midPoint,1]-1) elasticity[is.nan(elasticity)] <- 0 finalAvgEl <- mean(elasticity) })) ANNModelList <- list() ANNModelList[["ANNModelgrid"]] <- grid ANNModelList[["ANNBestModel"]] <- h2obest_model ANNModelList[["training"]] <- Training.hex ANNModelList[["testing"]] <- Testing.hex ANNModelList[["Elasticity"]] <- data.frame(Variable = h2obest_model@parameters$x, ANN_Elasticity = Elasticity,row.names = NULL,stringsAsFactors = F) return(ANNModelList) } # Generate Compared AVM Table ML_generateCompareAVM <- function(ML_ModelDataList,ML_ModelParameterList,SelectedModel){ ML_allAVM <- data.frame() # Actual ML_allAVM <- rbind(ML_ModelDataList$ML_ModelDF[ML_ModelParameterList[["Dependent"]]]) names(ML_allAVM) <- "Actual" # OLS Fitted if(any(names(ML_ModelDataList) %in% "ExternalModel")){ olsTrainFitted <- ML_ModelDataList$ExternalModel$ExternalModel$fitted.values olsTestFitted <- predict(ML_ModelDataList$ExternalModel$ExternalModel,ML_ModelDataList$ExternalModel$testing) if(SelectedModel[SelectedModel$ModellingType == "ExternalModel",3] == "MeanNorm"){ olsTrainFitted <- olsTrainFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) olsTestFitted <- olsTestFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) } ML_allAVM <- cbind(ML_allAVM,data.frame(ExternalModel_Fitted = c(olsTrainFitted,olsTestFitted))) } # GBM Fitted if(any(names(ML_ModelDataList) %in% "GBMModel")){ gbmTrainFitted <- predict.gbm(ML_ModelDataList$GBMModel$GBMModel,ML_ModelDataList$GBMModel$training) gbmTestFitted <- predict.gbm(ML_ModelDataList$GBMModel$GBMModel,ML_ModelDataList$GBMModel$testing) if(SelectedModel[SelectedModel$ModellingType == "GBM",3] == "MeanNorm"){ gbmTrainFitted <- gbmTrainFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) gbmTestFitted <- gbmTestFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) } ML_allAVM <- cbind(ML_allAVM,data.frame(GBM_Fitted = c(gbmTrainFitted,gbmTestFitted))) } # XGBoost Fitted if(any(names(ML_ModelDataList) %in% "XGBoostModel")){ xgbTrainFitted <- predict(ML_ModelDataList$XGBoostModel$best_model,xgb.DMatrix(data = sparse.model.matrix(Dependant ~ ., data = ML_ModelDataList$XGBoostModel$training), label = ML_ModelDataList$XGBoostModel$training[,"Dependant"])) xgbTestFitted <- predict(ML_ModelDataList$XGBoostModel$best_model,xgb.DMatrix(data = sparse.model.matrix(Dependant ~ ., data = ML_ModelDataList$XGBoostModel$testing), label = ML_ModelDataList$XGBoostModel$testing[,"Dependant"])) if(SelectedModel[SelectedModel$ModellingType == "XGBoost",3] == "MeanNorm"){ bayesTrainFitted <- bayesTrainFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) xgbTestFitted <- xgbTestFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) } ML_allAVM <- cbind(ML_allAVM,data.frame(XGBoost_Fitted = c(xgbTrainFitted,xgbTestFitted))) } # ANN Fitted if(any(names(ML_ModelDataList) %in% "ANNModel")){ #h2obest_model <- h2o.getModel(ML_ModelDataList$ANNModel$ANNModelgrid@model_ids[[1]]) h2obest_model <- ML_ModelDataList$ANNModel$ANNBestModel ANNTrainFitted <- as.data.frame(h2o.predict(h2obest_model, ML_ModelDataList$ANNModel$training)) ANNTestFitted <- as.data.frame(h2o.predict(h2obest_model, ML_ModelDataList$ANNModel$testing)) if(SelectedModel[SelectedModel$ModellingType == "ANN",3] == "MeanNorm"){ ANNTrainFitted <- ANNTrainFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) ANNTestFitted <- ANNTestFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) } ML_allAVM <- cbind(ML_allAVM,data.frame(ANN_Fitted = rbind(ANNTrainFitted,ANNTestFitted))) names(ML_allAVM)[names(ML_allAVM) %in% "predict"] <- "ANN_Fitted" } # Bayesian Fitted if(any(names(ML_ModelDataList) %in% "BayesianModel")){ bayesTrainFitted <- predict(ML_ModelDataList$BayesianModel$bayes_model)[,1] bayesTestFitted <- predict(ML_ModelDataList$BayesianModel$bayes_model,ML_ModelDataList$BayesianModel$testing)[,1] if(SelectedModel[SelectedModel$ModellingType == "Bayesian",3] == "MeanNorm"){ bayesTrainFitted <- bayesTrainFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) bayesTestFitted <- bayesTestFitted * mean(ML_ModelDataList[["ML_ModelDF"]][,ML_ModelParameterList[["Dependent"]]]) } ML_allAVM <- cbind(ML_allAVM,data.frame(Bayesian_Fitted = c(bayesTrainFitted,bayesTestFitted))) } # Bayesian Fitted if(any(names(ML_ModelDataList) %in% "BayesianBeliefNetworkModel")){ # bayesFitted <- predict(ML_ModelDataList$BayesianModel$bayes_model)[,1] # ML_allAVM <- cbind(ML_allAVM,data.frame(Bayesian_Fitted = c(bayesFitted,rep(0,nrow(ML_allAVM)-length(bayesFitted))))) } return(ML_allAVM) } # Generate Compared Elasticity Table ML_getCompareElasticityGrid <- function(ML_ModelDataList){ ElasticityGrid <- data.frame(Variable = ML_ModelDataList$BMAModelResult$Variable[ML_ModelDataList$BMAModelResult$InModel ==TRUE]) # OLS Fitted if(any(names(ML_ModelDataList) %in% "ExternalModel")){ ElasticityGrid <- merge(ElasticityGrid,ML_ModelDataList$ExternalModel$Elasticity,by = "Variable",all = T,sort = F) } # GBM Fitted if(any(names(ML_ModelDataList) %in% "GBMModel")){ ElasticityGrid <- merge(ElasticityGrid,ML_ModelDataList$GBMModel$Elasticity,by = "Variable",all = T,sort = F) } # XGBoost Fitted if(any(names(ML_ModelDataList) %in% "XGBoostModel")){ ElasticityGrid <- merge(ElasticityGrid,ML_ModelDataList$XGBoostModel$Elasticity,by = "Variable",all = T,sort = F) } # ANN Fitted if(any(names(ML_ModelDataList) %in% "ANNModel")){ ElasticityGrid <- merge(ElasticityGrid,ML_ModelDataList$ANNModel$Elasticity,by = "Variable",all = T,sort = F) } # Bayesian Fitted if(any(names(ML_ModelDataList) %in% "BayesianModel")){ ElasticityGrid <- merge(ElasticityGrid,ML_ModelDataList$BayesianModel$Elasticity,by = "Variable",all = T,sort = F) } ElasticityGrid <- ElasticityGrid[ElasticityGrid$Variable != "(Intercept)",] ElasticityGrid[,-1] <- apply(as.data.frame(ElasticityGrid[,-1]),2,function(x) round(x,digits = 3)) return(ElasticityGrid) } # Compare Models Stat ML_getCompareModelsStat <- function(ML_ModelDataList){ ModelStats <- list() # OLS Fitted if(any(names(ML_ModelDataList) %in% "ExternalModel")){ ##### MAPE #### olsStat <- list() ML_ModelDataList$ExternalModel$ExternalModel$model olsStat[["MAPE"]] <- mape(x = ML_ModelDataList$ExternalModel$ExternalModel$model[,1],y = ML_ModelDataList$ExternalModel$ExternalModel$fitted.values) ##### RMSE ##### olsStat[["RMSE"]] <- rmse(y = ML_ModelDataList$ExternalModel$ExternalModel$fitted.values,x = ML_ModelDataList$ExternalModel$ExternalModel$model[,1]) olsStat[["R2"]] <- paste0(round(summary(ML_ModelDataList$ExternalModel$ExternalModel)$r.squared,digits = 3) * 100,"%") ModelStats[["External Model"]] <- olsStat } # GBM Fitted if(any(names(ML_ModelDataList) %in% "GBMModel")){ gbmStat <- list() gbmStat[["MAPE"]] <- mape(x = ML_ModelDataList$GBMModel$training[,1],y = ML_ModelDataList$GBMModel$GBMModel$fit) gbmStat[["RMSE"]] <- rmse(y = ML_ModelDataList$GBMModel$GBMModel$fit,x = ML_ModelDataList$GBMModel$training[,1]) gbmStat[["R2"]] <- paste0(round((cor(ML_ModelDataList$GBMModel$GBMModel$fit,ML_ModelDataList$GBMModel$training[,1]))^2,digits = 3) * 100,"%") ModelStats[["GBM Model"]] <- gbmStat } # XGBoost Fitted if(any(names(ML_ModelDataList) %in% "XGBoostModel")){ xgbStat <- list() actual <- ML_ModelDataList$XGBoostModel$training[,1] fitted <- predict(ML_ModelDataList$XGBoostModel$best_model,xgb.DMatrix(data = sparse.model.matrix(Dependant ~ ., data = ML_ModelDataList$XGBoostModel$training), label = ML_ModelDataList$XGBoostModel$training[,"Dependant"])) xgbStat[["MAPE"]] <- mape(x = as.vector(actual),y = as.vector(fitted)) xgbStat[["RMSE"]] <- rmse(y = fitted,x = actual) xgbStat[["R2"]] <- paste0(round((cor(fitted,actual))^2,digits = 3) * 100,"%") ModelStats[["XGBoost Model"]] <- xgbStat } # ANN Fitted if(any(names(ML_ModelDataList) %in% "ANNModel")){ annStat <- list() actual = ML_ModelDataList$ANNModel$training[,1] fitted = h2o.predict(ML_ModelDataList$ANNModel$ANNBestModel, ML_ModelDataList$ANNModel$training) annStat[["MAPE"]] <- mape(x = as.vector(actual),y = as.vector(fitted)) annStat[["RMSE"]] <- rmse(y = as.vector(fitted),x = as.vector(actual)) annStat[["R2"]] <- paste0(round((cor(fitted,actual))^2,digits = 3) * 100,"%") ModelStats[["ANN Model"]] <- annStat } # Bayesian Fitted if(any(names(ML_ModelDataList) %in% "BayesianModel")){ bayesStat <- list() actual <- ML_ModelDataList$BayesianModel$bayes_model$data[,1] fitted <- predict(ML_ModelDataList$BayesianModel$bayes_model)[,1] bayesStat[["MAPE"]] <- mape(x = as.vector(actual),y = as.vector(fitted)) bayesStat[["RMSE"]] <- rmse(y = fitted,x = actual) bayesStat[["R2"]] <- paste0(round((cor(fitted,actual))^2,digits = 3) * 100,"%") ModelStats[["Bayesian Model"]] <- bayesStat } ModelStats <- do.call(rbind,lapply(names(ModelStats), function(x){ data.frame(Model = x,as.data.frame.list(ModelStats[[x]])) })) return(ModelStats) } ###################################################################### ###################### Functions Related to EDA ###################### ###################################################################### ################ Function to plot a Scatterplot kFunBiScatterPlot<-function(column,df){ p<- ggplot(df,aes_string(x=column[1],y=column[2]))+ geom_point()+ theme(axis.text=element_text(size=10,colour = "blue"), axis.title = element_text(size=15,face = "bold")) scale_color_gradient(low = "#0091ff", high = "#f0650e") return(p) } ################## Function to check correlation between Variables KFuncorrelation<-function(column,df) { r<-round(cor(df[column],df[column]),3) return(r) } crosstbl<-data.frame() ############# Function to check Contigency between Variables KFunContingencyTbl<-function(column,df) { crosstbl<-table(df[[column[1]]],df[[column[2]]]) crosstbl <- data.frame(cbind(variable=rownames(crosstbl),crosstbl)) rownames(crosstbl) <- NULL return(crosstbl) } ################## Function to create chisquare kFunChiSquare<-function(column,df){ kVarchisquaredf<-data.frame() data<-subset(MEUserProjectList$meRawData,select = column) t<-table(data) test<-chisq.test(t) kVarchisquaredf<-data.frame(pvalue=test$p.value,xsquared=test$statistic,degreeOfFreedom=test$parameter) return(kVarchisquaredf) } ######### Function for ANOVA ############## Function to find skewness and curtosis kFunStatsConCat<-function(df){ kVartble<-data.frame() kVartble<-data.frame((group_by(df, df[[2]]) %>% summarise(count = n(), mean = mean(df[[1]], na.rm = TRUE), sd = sd(df[[1]], na.rm = TRUE) ))) colnames(df[2]) <- names(kVartble[1]) return(kVartble) } ################## Function to plot a Boxplot KFunVisualize<-function(df) { p1<- ggboxplot(df, x = names(df)[2], y = names(df)[1],color = names(df)[2])+ theme(axis.text.x = element_text(face="bold", color="#993333",size=8, angle=45),plot.margin = margin(1,2,4,1,"cm")) return(p1) } ######### Function to display the summary of ANOVA KFunAnnovaTest<-function(df) { res.aov<-aov(df[[1]]~df[[2]]) return(summary(res.aov)) } ################## Function to plot a Heatmap kFunheatmap<-function(inheatmap,df){ df<-subset(df,select=inheatmap) cormat<-cor(df) melted_cordata <- melt(cormat) col.plot<-c('red','green') plot <- ggplot(data = melted_cordata, aes(x=Var1, y=Var2, fill=value, label= value))+ scale_fill_gradient(low="#58D68D",high="#FA8072")+theme(axis.text.x = element_text(size=8, angle=45),plot.margin = margin(1,2,6,1,"cm"))+theme(axis.text.y = element_text(size=8, angle=25),plot.margin = margin(1,2,6,1,"cm")) plot_tile<-plot+geom_tile() return(ggplotly(plot_tile)) } ######### Function to display the summary of ANOVA KFunAnnovaTest<-function(df){ res.aov<-aov(df[[1]]~df[[2]]) return(tidy(res.aov)) } ####### Function to craete a formula kFunanovaform<-function(contvar){ form<-as.formula(paste(contvar[1],"~",as.character(paste(contvar[2:length(contvar)],collapse = "+")))) return(form) } kFunAnovaMulti<-function(inaov,df){ res.aov<-aov(kFunanovaform(inaov),df) return(tidy(res.aov)) } ###################### Function related to DLM ###################### MovingAverage <- function(series,min,max){ if(max-min ==0){ Movavgdata <- series }else{ series <- as.data.frame(series) avgseries <- min:max avgseries <- avgseries[which((min:max)%%2 ==1)] avgseries <- avgseries[which(avgseries>1)] Movavgdata <- as.data.frame(replicate(expr = series[,1],n = length(avgseries)),stringsAsFactors = F) Movavgdata <- data.frame(do.call(cbind,replicate(Movavgdata,n= ncol(series))),stringsAsFactors = F) avgseries <- rep(avgseries,times= ncol(series)) names(Movavgdata) <- paste0(names(series),"_CMA_",avgseries) names(avgseries) <- names(Movavgdata) for(name in names(Movavgdata)){ #name <- names(Movavgdata)[1] Movavgdata[,name] <- movavg(x = Movavgdata[,name],n = avgseries[name],type = "s") } Movavgdata <- as.data.frame(Movavgdata) return(Movavgdata) } } lagTransformation <- function(series,min,max){ FinalLagdata <- data.frame() if(max-min== 0){ FinalLagdata <- series }else { series <- as.data.frame(series) Ldata <- as.data.frame(replicate(expr = series[,1],n = c(max - min + 1)),stringsAsFactors = F) #which(!names(DLMAfData) %in% c("Geography","Period")) names(Ldata) <- paste0(names(series),"_L",min:max) lagseries <- as.numeric(as.numeric(as.character(min)):as.numeric(as.character(max))) names(lagseries) <- names(Ldata) LagDataframe <- as.data.frame(Ldata) for(namel in names(lagseries)){ #namel <- names(lagseries)[1] if(!is.na(lagseries[namel])){ LagDataframe[,namel] <- shift(LagDataframe[namel],as.numeric(unname(lagseries[namel])),fill = 0,type = "lag") } } if(nrow(FinalLagdata)==0){ FinalLagdata <- LagDataframe }else{ FinalLagdata <- cbind(FinalLagdata,LagDataframe) } return(FinalLagdata) } } logTransformation <- function(series,value){ if(value ==0 ){ series <- series }else{ series <- as.data.frame(series) #value <- 2 #series[,1] <- log(series[,1],base = value) series[,1] <- ifelse(series[,1]==0,0,log(series[,1],base = value)) names(series) <- paste0(names(series),"_log_",value) return(series) } } PastAverage <- function(series,min,max){ if(max-min == 0){ Pastavgdata <-series }else{ series <- as.data.frame(series) pastseries <- min:max pastseries <- pastseries[which(pastseries>1)] Pastavgdata <- as.data.frame(replicate(expr = series[,1],n = length(pastseries)),stringsAsFactors = F) Pastavgdata <- data.frame(do.call(cbind,replicate(Pastavgdata,n = ncol(series))),stringsAsFactors = F) names(Pastavgdata) <- paste0(names(series),"_PA_",pastseries) pastseries <- rep(pastseries,times= ncol(Pastavgdata)) names(pastseries) <- names(Pastavgdata) #series1 <- data.frame(matrix(data = NA,nrow = nrow(series),ncol = 1)) #value <- 2 for(name in names(Pastavgdata)){ #name <- names(Pastavgdata)[1] for(i in pastseries[name]:nrow(Pastavgdata[name])){ Pastavgdata[i,name] <- sum(Pastavgdata[(i-pastseries[name]):(i-1),name])/2 } Pastavgdata[1:pastseries[name],name]<-0 } return(Pastavgdata) } } Normalization <- function(series){ series <- as.data.frame(series) mean <- sapply(X = series,FUN =mean) # mean <- mean(series[,1],na.rm = T) sd <- sapply(X = series,FUN =sd) # sd <- sd(series[,1]) NormalizedData <- data.frame() for(name in names(series)){ # name <-names(series)[1] series2 <- data.frame(matrix(data = 0,nrow = nrow(series),ncol = 1)) names(series2) <- name series2[1,name] <- 0 #series2[2:nrow(series),] <- (series[2:nrow(series),]-mean)/sd series2[2:nrow(series),name] <- ifelse(sd[name]==0,0,(series[2:nrow(series),name]-mean[name])/sd[name]) series2 <- as.data.frame(series2) names(series2) <- paste0(name,"_norm") if(nrow(NormalizedData) == 0){ NormalizedData <- series2 }else{ NormalizedData <- cbind(series2,NormalizedData) } } return(NormalizedData) } AdStock <- function(series,min,max,steps){ if(max-min == 0){ AdStockData <- series }else{ series <- as.data.frame(series) if(as.numeric(steps) == 0 | as.numeric(steps) == 1){ steps <- 1 AdStockSeries <- as.numeric(min) }else{ steps <- (as.numeric(max)-as.numeric(min))/(as.numeric(steps)-1) AdStockSeries <- as.numeric(seq(from=as.numeric(min),to=as.numeric(max),by=steps)) } series[1,] <- 0 AdStockData <- do.call(cbind, replicate(series, n = length(AdStockSeries), simplify=FALSE)) # names(AdStockData) <- paste0(names(series),"_A",AdStockSeries) names(AdStockData) <- as.character(sapply(names(series), function(x) paste0(x,"_A",AdStockSeries))) AdStockSeries <- rep(AdStockSeries,ncol(series)) names(AdStockSeries) <- names(AdStockData) for(name in names(AdStockData)){ #name <- names(AdStockData)[2] for( i in 2:length(AdStockData[,name])){ #i <- 2 AdStockData[i,name] <- (AdStockData[i,name] * as.numeric(AdStockSeries[name]) + ((1-as.numeric(AdStockSeries[name])*AdStockData[i-1,name]))) } } return(AdStockData) } }

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estimateprops.R

suppressWarnings(suppressPackageStartupMessages(library(xbioc))) suppressWarnings(suppressPackageStartupMessages(library(MuSiC))) suppressWarnings(suppressPackageStartupMessages(library(reshape2))) suppressWarnings(suppressPackageStartupMessages(library(cowplot))) ## We use this script to estimate the effectiveness of proportion methods ## Load Conf args <- commandArgs(trailingOnly = TRUE) source(args[1]) ## Estimate cell type proportions est_prop <- music_prop( bulk.eset = bulk_eset, sc.eset = scrna_eset, clusters = celltypes_label, samples = samples_label, select.ct = celltypes, verbose = T) ## Show different in estimation methods ## Jitter plot of estimated cell type proportions jitter.fig <- Jitter_Est( list(data.matrix(est_prop$Est.prop.weighted), data.matrix(est_prop$Est.prop.allgene)), method.name = methods, title = "Jitter plot of Est Proportions") ## Make a Plot ## A more sophisticated jitter plot is provided as below. We separated ## the T2D subjects and normal subjects by their HbA1c levels. m_prop <- rbind(melt(est_prop$Est.prop.weighted), melt(est_prop$Est.prop.allgene)) colnames(m_prop) <- c("Sub", "CellType", "Prop") m_prop$CellType <- factor(m_prop$CellType, levels = celltypes) # nolint m_prop$Method <- factor(rep(methods, each = 89 * 6), levels = methods) # nolint m_prop$HbA1c <- rep(bulk_eset$hba1c, 2 * 6) # nolint m_prop <- m_prop[!is.na(m_prop$HbA1c), ] m_prop$Disease <- factor(sample_groups[(m_prop$HbA1c > 6.5) + 1], # nolint levels = sample_groups) m_prop$D <- (m_prop$Disease == # nolint sample_disease_group) / sample_disease_group_scale m_prop <- rbind(subset(m_prop, Disease == healthy_phenotype), subset(m_prop, Disease != healthy_phenotype)) jitter.new <- ggplot(m_prop, aes(Method, Prop)) + geom_point(aes(fill = Method, color = Disease, stroke = D, shape = Disease), size = 2, alpha = 0.7, position = position_jitter(width = 0.25, height = 0)) + facet_wrap(~ CellType, scales = "free") + scale_colour_manual(values = c("white", "gray20")) + scale_shape_manual(values = c(21, 24)) + theme_minimal() ## Plot to compare method effectiveness ## Create dataframe for beta cell proportions and HbA1c levels m_prop_ana <- data.frame(pData(bulk_eset)[rep(1:89, 2), phenotype_factors], ct.prop = c(est_prop$Est.prop.weighted[, 2], est_prop$Est.prop.allgene[, 2]), Method = factor(rep(methods, each = 89), levels = methods)) colnames(m_prop_ana)[1:4] <- phenotype_factors m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_gene])) m_prop_ana$Disease <- factor(sample_groups[( # nolint m_prop_ana[phenotype_gene] > 6.5) + 1], sample_groups) m_prop_ana$D <- (m_prop_ana$Disease == # nolint sample_disease_group) / sample_disease_group_scale jitt_compare <- ggplot(m_prop_ana, aes_string(phenotype_gene, "ct.prop")) + geom_smooth(method = "lm", se = FALSE, col = "black", lwd = 0.25) + geom_point(aes(fill = Method, color = Disease, stroke = D, shape = Disease), size = 2, alpha = 0.7) + facet_wrap(~ Method) + ggtitle(compare_title) + theme_minimal() + scale_colour_manual(values = c("white", "gray20")) + scale_shape_manual(values = c(21, 24)) pdf(file = outfile_pdf, width = 8, height = 8) plot_grid(jitter.fig, jitter.new, labels = "auto", ncol = 1, nrow = 2) jitt_compare dev.off() ## Summary table for (meth in methods) { ##lm_beta_meth = lm(ct.prop ~ age + bmi + hba1c + gender, data = ##subset(m_prop_ana, Method == meth)) lm_beta_meth <- lm(as.formula( paste("ct.prop", paste(phenotype_factors, collapse = " + "), sep = " ~ ")), data = subset(m_prop_ana, Method == meth)) print(paste0("Summary: ", meth)) capture.output(summary(lm_beta_meth), file = paste0("report_data/summ_", meth, ".txt")) }